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Well, the obvious place to start was that little paper where scientists used CRISPR technology to show that Homo naledi buried their dead next to coursing rivers on Mars. Despite that slam-dunk of a choice, my vote for the most interesting/important piece of science news comes in two parts, spanning the last twenty-five months.

Part I: The plot setup

Back to December 2013, a one-year-old boy in a Guinean village died an agonizing death and, as a result, large numbers of people in the West learned the names of some West African countries for the first time. By now, everyone is familiar with the broad features of the West African Ebola virus epidemic. The disease, which previously had barely been on the public’s radar, had been flaring up intermittently in Central Africa and then quickly burning out. Its disease was devastatingly lethal, rapidly killing the majority of people infected; the virus requires contact with bodily fluid for transmission, and it has evolved brilliantly to facilitate that, as sufferers die in an explosion of bodily fluids—projectile vomiting, continuous diarrhea, and external hemorrhaging with some viral strains. If Joseph Conrad had known about Ebola, he would have written it into the story line in Heart of Darkness.

And then the virus made the nightmare jump from sporadic cases in the darkness of low density rain forest populations to its debut in the light of high density urban settings, bursting out almost simultaneously in the cities and towns of Sierra Leone, Liberia, and Guinea. It was inevitable, given modern mobility.

The countries were utterly unprepared—big surprise—as if anyone would be. Poor, with large shantytown populations, still recovering from years of war, minimal medical infrastructure. Liberia had all of around fifty doctors at the time.

It was a disaster on a breathtaking scale. Nearly 30,000 cases, over 11,000 deaths—numbers that are probably underestimates. By most analyses, an even larger number of people sickened and died in the secondary death toll due to hospitals being overwhelmed solely handling Ebola patients. At its peak, there were thousands of new cases each week.

The impact was staggering. Entire extended families were wiped out, as people cared for relatives, or washed their corpses, as per custom. Villages and towns were emptied, the capitals became ghost towns as governments urged people to stay in their homes, to not touch other people. What semblance there was of healthy economies in the countries was demolished. Hysteria flared in various predictable ways. One extreme was to deny the existence of the disease, insist it was a hoax—some quarantine centers were ransacked by crowds intent on “liberating” their relatives, burial teams were attacked when they came for bodies. Other forms took shunning, ostracizing, even brutally driving out virus-free survivors. And naturally, suspicion and fear of the disease prompted various shoot-the-messenger scenarios; in a machete-the-messenger variant, eight aid workers were hacked to death in a village in Guinea.

Naturally, suspicion and fear also played out on a larger level of conspiracy theories. No less a combination of a leading newspaper in Liberia, a Liberian professor in the US, Pravda’s English edition and Louis Farrakhan all declared that Ebola was invented by the West. As a bioweapon that escaped from a lab. As a bioweapon intentionally being field tested on Africans. As a strategy to decrease the number of Africans. Or, in a capitalist critique from Pravda showing a seeming nostalgia for Soviet-era thinking, as a bioweapon to be released, allowing the West to then hold the world hostage by charging exorbitant prices for the already patented cure—i.e., a military-pharmacology complex. And of course, various West African clergy weighed in with their discovery that God was using Ebola to punish West African countries for the supposed laxity with which they persecuted homosexuals.

Unless you were one of these conspiracy theorists, the story had no villains (amid the World Health Organization getting a lot of criticism for not being prepared and being slow off the mark; how in hell are you supposed to be prepared for something like that? they retorted). But there were heroes of enormous magnitude. Doctors Without Borders deservedly achieved cult status, both for its presence and effectiveness, but also for its honesty and lack of institutional BS. Medical missionaries were extraordinary (something I was initially loath to admit; having worked regularly in Africa for thirty years, I spout a secular leftist’s obligatory condemnation of missionaries). And most of all, there were the heroics of the West African health care workers—the doctors, nurses, ambulance drivers, burial teams—short of every resource that would make them effective or keep them safe. Ten percent of the deaths in the epidemic were of health care workers; in August 2014, Science published an Ebola paper where five of its West African authors were already dead.

We watched it all from afar, with the option to turn the page to the next story. And then it came home.

First were the handful of expatriate health care workers who became infected and were brought, amid extraordinary care and containment methods, back to the West for almost always successful treatment.

Then came Thomas Eric Duncan, a Liberian man visiting relatives in Dallas, whose Ebola was the first case diagnosed in the US. He was a good Samaritan who drove a sick, pregnant neighbor to the hospital, unaware that she had Ebola and thinking that her illness was pregnancy-related. Thus, he failed to note his exposure on health forms and was allowed to fly; he went to an ER in Dallas with the starts of the disease and someone (the finger pointing continues) failed to ask him if he had come from another country, let alone from the West African hot zone. He was sent home with an antibiotic prescription and returned to the hospital a few days later, dying. My god, for a few days, America learned the name of some African guy and worried about his health.

Then came the Ebola cases in two of the nurses that cared for him. The second, aware of the first having been diagnosed, nevertheless chose to fly to Akron to buy a wedding dress, resulting in the shop of thirty years tenure going out of business soon after because of people’s fears (in a move that defines chutzpah, the nurse joined the array of other customers of the wedding shop in requesting a refund on her ostensibly tainted purchase).

Then came the doctor, back from treating Ebola in West Africa, on the loose in New York City, going to a bowling alley in Brooklyn the evening before he developed symptoms. And the returning nurse who either did or didn’t represent a health risk, and who refused to be quarantined, biking around her Maine town for photographers, asymptomatic and virus free, wearing her bike helmet.

By then, we were all collectively wetting our pants with terror, and the obligatory question that would appear at the end of old articles about that obscure Central African hemorrhagic virus, “Could it happen here?” had become, “it’s happening here, isn’t it?”

And that produced what I consider to be the most important, significant moment concerning science in 2014. It hadn’t happened for Legionnaire’s disease, toxic shock syndrome, the anthrax scare, or even AIDS. It sure hasn’t happened for global warming. For the first time in my lifetime, America had a collective realization—we may all die horribly unless some scientists figure out a way to save us.

Part II: The (starts of a) resolution

As the most scientifically significant moment of 2015, a twenty-eight-author team publishes in Lancet about the results of a Phase II clinical trial of an Ebola vaccine. Nearly 8,000 Guinean subjects, careful experimental design, 100 percent effectiveness at preventing disease occurrence when administered immediately after exposure to someone with Ebola. Yes, this isn’t the end of the disease, and the research started long before the West African epidemic. But this is a rough approximation of scientists, with lightning speed, saving us. It would be nice if the general public thought the same.

A man (non-dad) conceived a child that did not match his (non-dad's) genotype, but the child's DNA was consistent with the child being the grandson of dad's parents. The proposed explanation is that dad had a twin brother (twin-dad) who was never born but whose cells colonized non-dad's testes when non-dad was a fetus. The cells of twin-dad produced the sperm that conceived the child.

In the modern era of sensitive genetic testing, multiple examples of chimerism are being detected where chimerism refers to a body containing cells derived from more than one fertilized egg. All of us probably contain replicating cells from more than one member of our genetic family. A distinction should be made between bodily individuals (who are chimeric) and genetic individuals who may be distributed across multiple bodies.

Completely unexpected—and hence potentially interesting—was my reaction to the scientific news in Simon Gächter and Benedikt Herrmann’s compelling paper entitled “Reciprocity, culture and human cooperation: previous insights and a new cross-cultural experiment” in the Philosophical Transactions of the Royal Society.

The authors were dealing with a classical question in social science, that is, the “Tragedy of the Commons,” or the conflict between individual interest and collective interest in dealing with common resources. This is a well established conundrum in contemporary behavioral economics and evolutionary sociobiology that is usually solved by (now) classical experimental results about cooperation, trust and altruistic punishment.

There is a vast literature showing how direct and indirect reciprocity are important tools for dealing with human cooperation. Many experiments have shown that people use “altruistic punishment” to sustain cooperation, that is, they are willing to pay without receiving anything back just in order to sanction those who don’t cooperate, and hence promote pro-social behavior.

Yet, Gächter and Herrmann showed in their surprising paper that in some cultures, when people were tested in cooperative games (such as the “public good game”), the people who cooperated were punished, rather than the free-riders.

In some societies, people prefer to act anti-socially and they take actions to make sure that the others do the same! This means that cooperation in societies is not always for the good: you can find cartels of anti-social people who don’t care at all for the common good and prefer to cooperate for keeping a status quo that suits them even if the collective outcome is a mediocre result.

As an Italian with first-hand experience living in a country where, if you behave well, you are socially and legally sanctioned, this news was exciting, even inspiring … perhaps cooperation is not an inherent virtue of the human species. Perhaps, in many circumstances, we prefer to stay with those who share our selfishness and weaknesses and to avoid pro-social altruistic individuals. Perhaps it's not abnormal to live outside a circle of empathy.

So what’s the scientific “news that stays news”: Cooperation for the collective worse is as widespread as cooperation for a better society!

Back in 1998, two groups of astrophysicists studying supernovae made one of the most important experimental discoveries of the 20th century: They found that empty space, vacuum, is not entirely empty. Each cubic centimeter of empty space contains about 10-29 grams of invisible matter, or, equivalently, vacuum energy. This is almost nothing, 29 orders of magnitude smaller than the mass of matter in a cubic centimeter of water, 5 orders of magnitude smaller than the proton mass. If the whole Earth would be made of such matter, it would weight less than a gram.

If vacuum energy is so small, how do we even know that it is there? Just try to put 10-29 grams on a most sensitive scale, at it will show nothing at all. At first, many people were skeptical about it, but then combined efforts of cosmologists who studied cosmic microwave background radiation and large scale structure of the universe not only confirmed this discovery, but allowed to measure energy density of vacuum with a few percent accuracy. Doubts and disbelief were replaced by acceptance, enthusiasm, and, finally, by the Nobel Prizes received in 2011 by Saul Perlmutter, Brian Schmidt and Adam Riess.

The news has shaken physicists all over the word. But is it much ado about nothing? If something is so hard to find, maybe it is irrelevant and not newsworthy?

Vacuum energy is extremely small indeed, but in fact it is comparable to the average energy density of normal matter in the universe. Prior to this discovery, astronomers believed that density of matter constituted only 30 percent of the density corresponding to a flat universe. This would mean that the universe is open, contrary to the prediction of the inflationary theory of the origin of the universe. The unexpected discovery of vacuum energy added the required 70 percent to the sum, thus confirming one of the most important predictions of inflationary cosmology.

The tiny vacuum energy is large enough to make our universe slowly accelerate. It will take more about 10 billion years for the universe to double in size, but if this expansion continues, in about 150 billion years all distant galaxies will run away from our galaxy so far that they will forever disappear from our view. That was quite a change from our previous expectations that in the future we are going to see more and more…

The possibility that vacuum may have energy was discussed almost a century ago by Einstein, but then he discarded the idea. Particle physicists re-introduced it again, but their best estimates of the vacuum energy density were way too large to be true. For a long time they were trying to find a theory explaining why vacuum energy must be zero, but all such attempts failed. Explaining why it is not zero, but incredibly, excruciatingly small, is a much greater challenge.

And there is an additional problem: At present, vacuum energy is comparable with the average energy density of matter in the universe. In the past, the universe was small, and vacuum energy was negligibly small compared to the energy density of normal matter. In the future, the universe will grow and density of normal matter will become exponentially small. Why do we live exactly at the time when the energy of empty space is comparable to the energy of normal matter?

Thirty years ago, well before the discovery of the energy of nothing, Steven Weinberg and several other scientists started to argue that observing a small value of the vacuum energy would not be too surprising: A universe with a large negative vacuum energy would collapse before life could have any chance to emerge, whereas a large positive vacuum energy would not allow galaxies to form. Thus we can only live in a universe with a sufficiently small absolute value of vacuum energy. But this anthropic argument by itself was not quite sufficient. We used to think that all parameters of the theory of fundamental interactions, such as vacuum energy, are just numbers, which are given to us and cannot change. That is why the vacuum energy was also called the cosmological constant. But if the vacuum energy is a true constant that cannot change, anthropic considerations cannot help.

The only presently known way to solve this problem was found in the context of the theory of inflationary multiverse and string theory landscape, which claims that our universe consists of many parts with different properties and with different values of vacuum energy. We can live only in those parts of the universe where the vacuum energy is small enough, which explains why the vacuum energy is so small in the part of the world where we live.

Some people are critical to this way of thinking, but during the 18 years since the discovery of the vacuum energy nobody came with a convincing alternative solution of this problem. Many others are very excited, including Steven Weinberg, who exclaimed: "Now we may be at a new turning point, a radical change in what we accept as a legitimate foundation for a physical theory."

This explanation of a small vacuum energy has an unexpected twist to it: According to this scenario, all vacua of our type are not stable, but metastable. This means that, in a distant future, our vacuum is going to decay, destroying life as we know it in our part of the universe, while recreating it over and over again it other parts of the world.

It is too early to say whether these conclusions are here to stay, or they will experience significant modifications in the future. In any case, it is amazing that the news of a seemingly inconsequential discovery of an incredibly small energy of empty space may have groundbreaking consequences for cosmology, string theory, scientific methodology, and even for our views of the ultimate fate of the universe.

Human intuition is a notoriously poor guide to reality. A half-century of psychological research has shown that when people try to assess risks or predict the future, their heads are turned by stereotypes, memorable events, vivid scenarios, and moralistic narratives.

Fortunately, as the bugs in human cognition have become common knowledge, the workaround—objective data—has become more prevalent, and in many spheres of life, observers are replacing gut feelings with quantitative analysis. Sports have been revolutionized by Moneyball, policy by Nudge, punditry by 538.com, forecasting by tournaments and prediction markets, philanthropy by effective altruism, the healing arts by evidence-based medicine.

This is interesting news, and it’s scientific news because the diagnosis comes from cognitive science and the cure from data science. But the most interesting news is that the quantification of life has been extended to the biggest question of all: Have we made progress? Have the collective strivings of the human race against entropy and the nastier edges of evolution succeeded in improving the human condition?

Enlightenment thinkers thought this was possible, of course, and in Victorian times progress became a major theme of Anglo-American thought. But since then, Romantic and counter-Enlightenment pessimism have taken over large swaths of intellectual life, stoked by historical disasters such as the World Wars, and by post-1960s concerns with anthropogenic problems such as pollution and inequality. Today it’s common to read about a "faith" in progress (often a "naïve" faith), which is set against a nostalgia for a better past, an assessment of present decline, and a dread for a dystopia to come.

But the cognitive and data revolutions warn us not to base our assessment of anything on subjective impressions or cherry-picked incidents. As long as bad things haven’t vanished altogether, there will always be enough to fill the news, and people will intuit that the world is falling apart. The only way to circumvent this illusion is to plot the incidence of good and bad things over time. Most people agree that life is better than death, health better than disease, prosperity better than poverty, knowledge better than ignorance, peace better than war, safety better than violence, freedom better than coercion. That gives us a set of yardsticks by which we can measure whether progress has actually occurred.

The interesting news is that the answer is mostly "yes." I had the first inkling of this answer when quantitative historians and political scientists responded to my answer to the 2007 Edge question ("What Are You Optimistic About?") with datasets showing that the rate of homicides and war deaths had plummeted over time. Since then I have learned that progress has been tracked by the other yardsticks. Economic historians and development scholars (including Gregory Clark, Angus Deaton, Charles Kenny, and Steven Radelet) have plotted the growth of prosperity in their data-rich books, and the case has been made even more vividly in websites with innovative graphics such as Hans Rosling’s Gapminder, Max Roser’s Our World in Data, and Marian Tupy’s HumanProgress.

Among the other upward swoops are these. People are living longer and healthier lives, not just in the developed world but globally. A dozen infectious and parasitic diseases are extinct or moribund. Vastly more children are going to school and learning to read. Extreme poverty has fallen worldwide from 85 to 10 percent. Despite local setbacks, the world is more democratic than ever. Women are better educated, marrying later, earning more, and in more positions of power and influence. Racial prejudice and hate crimes have decreased since data were first recorded. The world is even getting smarter: In every country, IQ has been increasing by three points a decade.

Of course, quantified progress consists of a set of empirical findings; it is not a sign of some mystical ascent or utopian trajectory or divine grace. And so we should expect to find some spheres of life that have remained the same, gotten worse, or are altogether unquantifiable (such as the endless number of apocalypses that may be conjured in the imagination). Greenhouse gases accumulate, fresh water diminishes, species go extinct, nuclear arsenals remain.

Yet even here, quantification can change our understanding. "Ecomodernists" such as Stewart Brand, Jesse Ausubel, and Ruth DeFries have shown that many indicators of environmental health have improved over the last half-century, and that there are long-term historical processes, such as the decarbonization of energy, the dematerialization of consumption, and the minimization of farmland that can be further encouraged. Tabulators of nuclear weapons have pointed out that no such weapon has been used since Nagasaki, testing has fallen effectively to zero, proliferation has expanded the club only to nine countries (rather than thirty or more, as was predicted in the 1960s), sixteen countries have given up their programs (Iran should soon be the seventeenth), and the number of weapons (and hence the number of opportunities for thefts and accidents, and the number of obstacles to the eventual goal of zero) has been reduced by five sixths.

What makes all this important? Foremost, quantified progress is a feedback signal for adjusting what we have been doing. The gifts of progress we have enjoyed are the result of institutions and norms that have become entrenched in the last two centuries: reason, science, technology, education, expertise, democracy, regulated markets, and a moral commitment to human rights and human flourishing. As counter-Enlightenment critics have long pointed out, there is no guarantee that these developments would make us better off. Yet now we know that in fact they have left us better off. This means that for all the ways in which the world today falls short of utopia, the norms and institutions of modernity have put us on a good track. We should work on improving them further, rather than burning them down in the conviction that nothing could be worse than our current decadence and in the vague hope that something better might rise from their ashes.

Also, quantified human progress emboldens us to seek more of it. A common belief among activists is that any optimistic datum must be suppressed lest it lull people into complacency. Instead, one must keep up the heat by wailing about ongoing crises and scolding people for being insufficiently terrified. Unfortunately, this can lead to a complementary danger: fatalism. After being told that the poor might always be with us, the gods will punish our hubris, nature will rise up and avenge our despoliation, and the clock is inexorably ticking down to a midnight of nuclear holocaust and climatic catastrophe, it’s natural to conclude that resistance is futile and we should party while we can. The empowering feature of a graph is that it invites one to identify the forces that are pushing a curve up or down, and then to apply them to push it further in the same direction.

One of the scientific heroes of our time is Pieter van Dokkum, professor in the Yale astronomy department and author of a recent book, Dragonflies. The book is about insects, illustrated with marvelous photographs of real dragonflies taken by van Dokkum in their natural habitats. As an astronomer he works with another kind of dragonfly. The Dragonfly Observatory is an array of ten sixteen-inch refractor telescopes arranged like the compound eye of an insect dragonfly. The refracting lenses are coated with optical surface layers designed to give them superb sensitivity to faint extended objects in the sky. For faint extended objects, the Dragonfly Observatory is about ten times more sensitive than the best large telescopes. The Dragonfly is also about a thousand times cheaper. The ten refractors cost together about a hundred thousand dollars, compared with a hundred million for a big telescope.

The Dragonfly Observatory recently finished a search for faint dwarf galaxies orbiting within the gravitational field of our own galaxy. About fifty dwarf companions to our galaxy were discovered, more than were expected from computer models of galactic evolution. Each dwarf galaxy is embedded in a halo of dark matter whose mass can be determined from the observed velocities of the visible stars. The dwarf galaxies have about a hundred times more dark mass than visible mass, compared with the ratio of ten to one between dark and visible mass in our own galaxy. The Dragonfly observations reveal a universe with an intense fine-structure of dark-matter clumps, much clumpier than the standard theory of big-bang cosmology had predicted.

So it happens that a cheap small observatory can make a big new discovery about the structure of the universe. If the cost-effectiveness of an observatory is measured by the ratio of scientific output to financial input, the Dragonfly wins by a large factor. This story has a moral. The moral is not that we should put all our money into small observatories. We still need big telescopes and big organizations to do world-class astronomy. The moral is that a modest fraction of the astronomy budget, perhaps as much as a third, should be reserved for small and cheap projects. From time to time a winner like Dragonfly will emerge.

Early this year an old friend, a professor of neurology, sent me an article from a medical journal, “Surgical Neurology International," at first glance, predictably, a concoction of specialist language. The “Turin Advanced Neuromodulation Group” is describing “Cephalosomatic Anastomosis” (CSA), to be performed with “a nanoknife made of a thin layer of silicon nitride with a nanometer sharp-cutting edge.”

Only slowly it becomes clear they are talking about something rather unexpected: “Kephale,” Greek for head, “Somatikos,” Greek for body, “Anastasis,” Latin for resurrection—that prosaic CSA stands for...a complete head transplantation. And that reverberated with me, the implications being literally mind-boggling.

There is that "most interesting" aspect: the thought of a functioning brain reconnected to an entirely new body does open up any number of speculations. And has done so in countless SciFi books and B movies. But there is a lot to consider.

The author, Italian surgeon Sergio Canavero, announced a few months later that he had a suitable donor for the head part, and suddenly made it sound quite real, adding tangible details: the operation was to be done in China, requiring a team of 150 specialists, taking 36+ hours and costing $15+ million...to be performed in 2017.

Then it really hit the mass media. Many responses revolved around the ethics of such an action, using the F-word a lot (and I mean “Frankenstein”) and debating the scientific details of the spinal cord fusion.

My stance on the ethical side is biased by a personal moment: In the mid-90s I visited Stephen Hawking in Cambridge for a project and he later visited in Santa Barbara—both interactions, up-close, left me with an overpowering impression. There was that metaphor of "the mind being trapped in a body," playing out in all its deep and poignant extreme—the most intelligent of minds weighed down so utterly by the near useless shell of a body. A deep sadness would overcome anyone witnessing that, far beyond the Hollywood movie adaption.

There is the rub then: who could possibly argue against this man choosing to lengthen his lifetime and gain a functioning body, should such an option exist? Could anyone deny him the right to try, if medicine were up to the task?

In reality Hawking is not a candidate even in theory, his head being afflicted by the disease as well, but he does serve as a touching and tragic example of that ethical side.

Another personal connection for me is this—imagine: critical voices called it “playing God.” Human hubris. Where would all the donors come from? Is this medicine just for the rich?

Now consider: the first operation leads to the recipient dying after eighteen days. It is being repeated and the subsequent 100 operations lead to nearly 90 percent of the patients not surviving the two-year mark.

No, that is not a prognostication for CSA, but I am recalling the events from nearly fifty years ago: It was December, 1967 when Christiaan Neethling Barnard performed the first human heart transplantation, the eyes of the world upon him, his face plastered on magazine covers across the globe. I was ten and remembered his double-voweled name as much as the unfathomable operation itself. He was met with exactly the same criticism, the identical ethical arguments.

After the dismal survival rate the initial enthusiasm turned around and a year later those condemning the practice were gaining. Only after the introduction of Cyclosporine to vastly improve the immune rejection issues did the statistics turn in his favor and tens of thousands such operations have since been performed.

I consider that an important aspect in this: every stage of progress has had critical voices loudly extrapolating curves into absurdity—back then, as now, there is immediate doomsday talk of “entire prison populations harvested for donors,” and such.

Sadly, watching videos of Canavero on the Web is rather cringeworthy. Slinging hyperbolae of "the world will never be the same," naming his procedure heaven and gemini, then squashing a banana representing a damaged spinal cord versus a neatly sliced one to illustrate his ostensibly easy plan. He repeatedly calls it "fusing spaghetti" and even assessed the chances for his Russian donor at "90 percent to walk again."

The Guardian mentions “he published a book, Donne Scoperte, or Women Uncovered, that outlined his tried-and-tested seduction techniques.” It seems rather clear that there is little place for levity when he belittles the details and glosses over the reality: millions of quadriplegic victims are eyeing very closely what the chances are to truly re-fuse spinal cords. That alone is probably worth a great deal of energy, effort, time and expense.

The story here is not about one celebrity poser. In my view it cannot happen by 2017, by far. But 2027, 37, 47? Looking backward you can see the increase in complexity that makes it almost inevitable to think: this will be possible. And then the truly interesting questions come into play...

If phantom limbs bring serious psychological issues, what would an entire phantom body conjure up? The self-image is such a subtle process, the complexity of signals, fluids, and messenger chemistry, how could it all possibly attain a state remotely stable, let alone "normal"?

Christiaan Barnard, who asked why anyone would choose such a risky procedure, said:

For a dying person, a transplant is not a difficult decision. If a lion chases you to a river filled with crocodiles, you will leap into the water convinced you have a chance to swim to the other side. But you would never accept such odds if there were no lion.

Me, I dread even just the waiting room at the dentist. But thirty years hence, maybe I would opt for the crocodiles as well. If Hawking can survive longer—by all means, he should. Some other characters I can think of—their best hope lies in acquiring a new head. Thus, I am of two minds about complete head transplants.

As Stewart Brand acutely says, most of the things that dominate the news are not really new: love, scandal, crime, and war come round again and again. Only science and invention deliver truly new stuff, like double helixes and search engines. In this respect, the new news from recent science that most intrigues me is that we may have a way to explain why certain diseases are getting worse as we get richer. We are defeating infectious diseases, slowing or managing many diseases of ageing like heart disease and cancer, but we are faced with a growing epidemic of allergy, auto-immunity, and things like autism. Some of it is due to more diagnosis, some of it is no doubt hypochondria, but there does seem to be a real increase in these kinds of problems.

Take hay fever. It is plainly a modern disease, far more common in urban, middle-class people than it used to be in peasants in the past, or still is in subsistence farmers in Africa today. There's really good timeline data on this, chronicling the appearance of allergies as civilization advances, province by province or village by village. And there’s really good evidence that what causes this is the suppression of parasites. You can see this happen in eastern Europe and in Africa in real time: get rid of worms and a few years later children start getting hay fever. Moises Velasquez-Manoff chronicles this in glorious detail in his fine book An Epidemic of Absence.

This makes perfect sense. In the arms race with parasites, immune systems evolved to “expect” to be down-regulated by parasites, so they over-react in their absence. A good balance is reached when parasites try down-regulating the immune system, but it turns rogue when there are no parasites.

And the obvious remedy works: ingest worms and you rid yourself of hay fever. Though it is probably not worth it—worms are no fun.

But how many of our modern diseases are caused by this problem—an impoverished ecology not just of parasites but of commensal and symbiotic micro-organisms too? Do kids today in the rich world have unbalanced gut flora after an upbringing of obsessive hygiene? Probably. How many diseases and disorders are the consequence of this? More than we think, I suspect—multiple sclerosis, obesity, anorexia, perhaps autism even.

There’s a fascinating recent study by Jeffrey Gordon's group at Washington University School of Medicine, St. Louis, showing that if you take the gut flora from an obese person and introduce it into a mouse with no gut flora, the mouse puts on weight faster than does another mouse with gut flora introduced from the obese person's non-obese twin: that's a well designed experiment.

So a big new thing in science is that we are beginning to understand the epidemic of absence.

These days see a tremendous number of significant scientific news, and it is hard to say which one has the highest significance. Climate models indicate that we are past crucial tipping points and are irrevocably headed for a new, difficult age for our civilization. Mark Van Raamsdonk expands on the work of Brian Swingle and Juan Maldacena, and demonstrates how we can abolish the idea of spacetime in favor of a discrete tensor network, thus opening the way for a unified theory of physics. Bruce Conklin, George Church and others have given us CRISPR, a technology that holds the promise for simple and ubiquitous gene editing. Deep Learning starts to tell us how hierarchies of interconnected feature detectors can autonomously form a model of the world, learn to solve problems, and recognize speech, images and video.

It is perhaps equally important to notice where we lack progress: sociology fails to teach us how societies work, philosophy seems to have become barren and infertile, the economical sciences seem to be ill-equipped to inform our economic and fiscal policies, psychology does not comprehend the logic of our psyche, and neuroscience tells us where things happen in the brain, but largely not what they are.

In my view, the 20th century’s most important addition to understanding the world is not positivist science, computer technology, spaceflight, or the foundational theories of physics. It is the notion of computation. Computation, at its core, and as informally described as possible, is very simple: every observation yields a set of discernible differences.

These, we call information. If the observation corresponds to a system that can change its state, we can describe these state changes. If we identify regularity in these state changes, we are looking at a computational system. If the regularity is completely described, we call this system an algorithm. Once a system can perform conditional state transitions and revisit earlier states, it becomes almost impossible to stop it from performing arbitrary computation. In the infinite case, that is, if we allow it to make an unbounded number of state transitions and use unbounded storage for the states, it becomes a Turing Machine, or a Lambda Calculus, or a Post machine, or one of the many other, mutually equivalent formalisms that capture universal computation.

Computational terms rephrase the idea of "causality," something that philosophers have struggled with for centuries. Causality is the transition from one state in a computational system into the next. They also replace the concept of "mechanism" in mechanistic, or naturalistic philosophy. Computationalism is the new mechanism, and unlike its predecessor, it is not fraught with misleading intuitions of moving parts.

Computation is different from mathematics. Mathematics turns out to be the domain of formal languages, and is mostly undecidable, which is just another word for saying uncomputable (since decision making and proving are alternative words for computation, too). All our explorations into mathematics are computational ones, though. To compute means to actually do all the work, to move from one state to the next.

Computation changes our idea of knowledge: instead of treating it as justified true belief, knowledge describes a local minimum in capturing regularities between observables. Knowledge is almost never static, but progressing on a gradient through a state space of possible world views. We will no longer aspire to teach our children the truth, because like us, they will never stop changing their minds. We will teach them how to productively change their minds, how to explore the never ending land of insight.

A growing number of physicists understand that the universe is not mathematical, but computational, and physics is in the business of finding an algorithm that can reproduce our observations. The switch from uncomputable, mathematical notions (such as continuous space) makes progress possible. Climate science, molecular genetics, and AI are computational sciences. Sociology, psychology, and neuroscience are not: they still seem to be confused by the apparent dichotomy between mechanism (rigid, moving parts) and the objects of their study. They are looking for social, behavioral, chemical, neural regularities, where they should be looking for computational ones.

Science is never fixed in place: it must always move forward, and in that sense it is always “news.” What makes science news in a journalistic sense, however, tends to be biased by current concerns, economic interests, and popular fears and hopes.

It is no surprise that research into the brain, in particular, continues to be the focus of much media attention—not only for the obvious reason of its central role in the very fabric of evolved life and of its infinite complexity, but also because of the culturally strong need to understand the biological bases of human behavior. This has led to many excessively positive claims for, and overinterpretations of, necessarily partial, provisional findings about brain mechanisms. The prefix “neuro” now twists into pseudo-scientific shape all aspects of human behavior, from aesthetics to economics, as if the putative cerebral correlates for all that we do explained to us what we are. Of course there are worthwhile and important avenues to explore here; but reports in the mainstream media can hardly do justice to their scientific, methodological, and conceptual complexity.

Yet truly newsworthy neuroscience does get reported. The publication in a June 2015 issue of Nature of the discovery of a lymphatic system within the central nervous system is hugely important, and was acknowledged as such in more mainstream venues. Science Daily titled its report of the discovery “Missing link found between brain, immune system; major disease implications,” with the blurb, “In a stunning discovery that overturns decades of textbook teaching, researchers have determined that the brain is directly connected to the immune system by vessels previously thought not to exist. The discovery could have profound implications for diseases from autism to Alzheimer's to multiple sclerosis.”

We might need to take with a few grains of salt this last sentence—the sort of claim that reflects comprehensible wishful thinking rather than actual reality, typical of what constitutes fast-burning “news.” On the other hand, few discoveries do “overturn decades of textbook learning”—and this one probably does. The fact that established learning can be explicitly overturned is important in itself, for it is easy to forget that most work goes on within given frameworks, on the basis of assumptions rather than with an eye to the need always to question precisely those assumptions that are taken for granted. This particular discovery emphasizes at last the need to understand connections between the nervous and immune systems, and can only push forward the development of the still merely burgeoning field of neuroimmunology. Precisely because of the highly specialized nature of research and clinical care, brain facts tend to be understood apart from body facts, in a Cartesian fashion, as if one were really apart from the other. This piece of news reminds us that we can only understand one as an aspect of the other; and that we need to take seriously, in scientific terms, phenomena such as the placebo and nocebo effects, and the role of the psyche in the evolution of mental and physical disease generally.

And in turn, this goes to show that what we each take to be scientific news—that is, news about our understanding of the world and ourselves—is a function of what we expect it to look like.

The new biotech tool called “gene drive” changes our relation to wild species profoundly. Any gene (or set of genes) can be forced to “drive” through an entire wild population. It doesn’t matter if the trait the genes control is deleterious to the organism. With one genetic tweak to its germline, a species can even be compelled to go extinct.

The technique works by forcing homozygosity. Once the genes for a trait are homozygous (present on both chomosomes) and the parents are both homozygous, they will breed true for that trait in all their descendents. Artificially selecting for desired traits via homozygosity is what breeders do. Now there’s a shortcut.

In effect, gene-drive genes forbid the usual heterozygosity in cross-bred parents. In any two parents, if one of them is gene-drive homozygous, all their offspring will be gene-drive homozygous and will express the gene-drive trait. Proviso: it only works with sexually reproducing species—forget bacteria. And it only spreads quickly enough in rapidly reproducing species—forget humans.

The mechanism was first described in 2003 as a potential tool by Austin Burt of Imperial College London. The way it works is that a “homing endonuclease gene” cuts the DNA in the adjoining chromosome and provides the template for the DNA repair, thus duplicating itself. In Richard Dawkins terms, it is an exceptionally selfish gene. Heterozygous becomes homozygous, and after several generations the gene is present in every individual of the population. The phenomenon is common in nature.

Gene drive shifted from an interesting concept to a powerful tool with the arrival in the last few years of a breakthrough in genome editing called CRISPR-Cas9. Suddenly genes could be edited easily, cheaply, quickly, and with great precision. It was a revolution in biotech.

In 2014 George Church and Kevin Esvelt at Harvard published three papers spelling out the potential power of CRISPR-enabled gene drive and the kind of public and regulatory oversight needed to ensure its responsible deployment. They also encouraged the development of an “undo” capability. Ideally the effects of an initial gene drive release could, if desired, be reversed before it spread too far with the release of a countermanding secondary gene drive.

The benefits of gene drive could be huge. Vector-borne scourges like malaria and dengue fever could be eliminated by eliminating (or just adjusting) the mosquitoes that carry them. Food crops could be protected by reversing herbicide-resistance in weeds. Wildlife conservation would be able to cure one of its worst threats—the alien invasive rats, mice, ants, etc. that are massively destructive to native species on ocean islands. With gene drive the invaders could be completely extirpated (driven extinct locally), and the natives would be protected permanently.

Developments are coming quickly. A team at Harvard proved that gene drive works in yeast. A team at UC San Diego inadvertently proved that it works in fruit flies. Most importantly, Anthony James at UC Irvine and colleagues showed that malaria mosquitoes could be altered with gene drive so that they no longer carry the disease. Kevin Esvelt is developing a project to do the same with white-footed mice, which are the wildlife reservoir for Lyme disease in humans; if they are cured, humans will be as well.

The power to permanently change wild populations genetically is a serious matter. There are ecological questions, ethical issues, and many technical nuances that have to be sorted out thoroughly. Carefully, gradually, they will be.

Humanity has decided about this sort of thing before. Guinea worms are a horrible parasite that used to afflict 2.5 million people, mostly in Africa. In 1980 disease control experts set about eliminating the worms totally from the world, primarily through improved water sanitation. That goal of deliberate extinction is now on the brink of completion. One of the strongest advocates of the project, President Jimmy Carter, declared publicly, “I would like the last Guinea worm to die before I do.”

Gene drive is not a new kind of power, but it is a new level of power. And a new level of responsibility.

If you were on a selection committee tasked with choosing someone to hire (or to admit to your university, or to receive a prize in your field), and it came down to two candidates who were equally qualified on objective measures, which candidate would you be most likely to choose?

__A) The one who shared your race
__B) The one who shared your gender
__C) The one who shared your religion
__D) The one who shared your political party or ideology

The correct answer, for most Americans, is now D. It is surely good news that prejudice based on race, gender, and religion are way down in recent decades. But it is very bad news—for America, for the world, and for science—that cross-partisan hostility is way up.

My nomination for “news that will stay news” is a paper by political scientists Shanto Iyengar and Sean Westwood, titled “Fear and Loathing Across Party Lines: New Evidence on Group Polarization.” Iyengar and Westwood report four studies (all using nationally representative samples) in which they gave Americans various ways to reveal both cross-partisan and cross-racial prejudice, and in all cases cross-partisan prejudice was larger.

First they used a measure of implicit attitudes (the Implicit Association Test), which measures how quickly and easily people can pair words that are emotionally good versus bad with words and images associated with Blacks vs. Whites. They also ran a new version of the test that swapped in words and images related to Republicans vs. Democrats, instead of Blacks vs. Whites. The effect sizes for cross-partisan implicit attitudes were much larger than cross-race. If we focus just on White participants who identified with a party, the cross-partisan effect was about 50 percent larger than the cross-race effect. When Americans look at each other or try to listen to each other, their automatic associations are more negative for people from the “other side” than they are for people of a different race.

In another study they had participants read pairs of fabricated resumes of graduating high school seniors and select one to receive a scholarship. Race made a difference—Black and White participants generally preferred to award the scholarship to the student with the stereotypically Black name. But Party made an even bigger difference, and always in a tribal way: 80 percent of the time, partisans selected the candidate whose resume showed that they were on their side, and it made little difference whether their co-partisan had a higher or lower GPA than the cross-partisan candidate.

In two additional studies Iyengar and Westwood had participants play behavioral economics games (the “trust game” and the “dictator game”). Each person played with what they thought was a particular other person, about whom they read a brief profile including the person’s age, gender, race, and political ideology. Race and ideology were manipulated systematically. Race made no difference, but partisanship mattered a lot: people were more trusting and generous when they thought they were playing with a co-partisan than a cross-partisan.

This is extremely bad news for America because it is very hard to have an effective democracy without compromise. But rising cross-partisan hostility means that Americans increasingly see the other side not just as wrong but as evil, as a threat to the very existence of the nation, according to Pew Research. Americans can expect rising polarization, nastiness, paralysis, and governmental dysfunction for a long time to come.

This is a warning for the rest of the world because some of the trends that have driven America to this point are occurring in many other countries, including: rising education and individualism (which make people more ideological), rising immigration and ethnic diversity (which reduces social capital and trust), and stagnant economic growth (which puts people into a zero-sum mindset).

This is extremely bad news for science and universities because universities are usually associated with the left. In the United States, universities have moved rapidly left since 1990, when the left-right ratio of professors across all departments was less than two to one. By 2004, the left-right ratio was roughly five to one, and it is still climbing. In the social sciences and humanities it is far higher. Because this political purification is happening at a time of rising cross-partisan hostility, we can expect increasing hostility from Republican legislators toward universities and the things they desire, including research funding and freedom from federal and state control.

Tribal conflicts and tribal politics took center stage in 2015. Iyengar and Westwood help us understand that tribal conflicts are no longer just about race, religion, and nationality. Cross-partisan prejudice should become a focus of concern and research. In the United States, it may even be a more urgent problem than cross-racial prejudice.

There’s a race going on that will determine the fate of humanity. Just as it’s easy to miss the forest for all the trees, however, it’s easy to miss this race for all the scientific news stories about breakthroughs and concerns. What do all these headlines from 2015 have in common?

They are all manifestations of the aforementioned race heating up: the race between the growing power of technology and the growing wisdom with which we manage it. The power is growing because our human minds have an amazing ability to understand the world and to convert this understanding into game-changing technology. Technological progress is accelerating for the simple reason that breakthroughs enable other breakthroughs: as technology gets twice as powerful, if can often be used to used to design and build technology that is twice as powerful in turn, triggering repeated capability doubling in the spirit of Moore’s law.

What about the wisdom ensuring that our technology is beneficial? We have technology to thank for all the ways in which today is better than the Stone Age, but this not only thanks to the technology itself but also thanks to the wisdom with which we use it. Our traditional strategy for developing such wisdom has been learning from mistakes: We invented fire, then realized the wisdom of having fire alarms and fire extinguishers. We invented the automobile, then realized the wisdom of having driving schools, seat belts and airbags.

In other words, it was OK for wisdom to sometimes lag behind in the race, because it would catch up when needed. With more powerful technologies such as nuclear weapons, synthetic biology and future strong artificial intelligence, however, learning from mistakes is not a desirable strategy: we want to develop our wisdom in advance so that we can get things right the first time, because that might be the only time we’ll have. In other words, we need to change our approach to tech risk from reactive to proactive. Wisdom needs to progress faster.

This year’s Edge Question “What is the most interesting recent news and what makes it important?” is cleverly ambiguous, and can be interpreted either as call to pick a news item or as asking about the very definition of “interesting and important news.” If we define “interesting” in terms of clicks and Nielsen ratings, then top candidates must involve sudden change of some sort, whether it be a discovery or a disaster. If we instead define “interesting” in terms of importance for the future of humanity, then our top list should include even developments too slow to meet journalist’s definition of “news,” such as “Globe keeps warming.” In that case, I’ll put the fact that the wisdom race is heating up at the very top of my list. Why?

From my perspective as a cosmologist, something remarkable has just happened: after 13.8 billion years, our universe has finally awoken, with small parts of it becoming self-aware, marveling at the beauty around them, and beginning to decipher how their universe works. We, these self-aware life forms, are using our new-found knowledge to build technology and modify our universe on ever grander scales.

This is one of those stories where we get to pick our own ending, and there are two obvious ones for humanity to choose between: either win the wisdom race and enable life to flourish for billions of years, or lose the race and go extinct. To me, the most important scientific news is that after 13.8 billion years, we finally get to decide—probably within centuries or even decades.

Since the decision about whether to win the race sounds like such a no-brainer, why are we still struggling with it? Why is our wisdom for managing technology so limited that we didn’t do more about climate change earlier, and have come close to accidental nuclear war over a dozen times? As Skype-founder Jaan Tallinn likes to point out, it is because our incentives drove us to a bad Nash equilibrium. Many of humanity’s most stubborn problems, from destructive infighting to deforestation, overfishing and global warming, have this same root cause: when everybody follows the incentives they are given, it results in a worse situation than cooperation would have enabled.

Understanding this problem is the first step toward solving it. The wisdom we need to avoid lousy Nash equilibria must be developed at least in part by the social sciences, to help create a society where individual incentives are aligned with the welfare of humanity as a whole, encouraging collaboration for the greater good. Evolution endowed us with compassion and other traits to foster collaboration, and when more complex technology made these evolved traits inadequate, our forebears developed peer pressure, laws and economic systems to steer their societies toward good Nash equilibria. As technology gets ever more powerful, we need ever stronger incentives for those who develop, control and use it to make its beneficial use their top priority.

Although the social sciences can help, plenty of technical work is needed as well in order to win the race. Biologists are now studying how to best deploy (or not) tools such as CRISPR genome editing. 2015 will be remembered as the year when the beneficial AI movement went mainstream, engendering productive symposia and discussions at all the largest AI-conferences. Supported by many millions of dollars in philanthropic funding, large numbers of AI-researchers around the world have now started researching the fascinating technical challenges involved in keeping future AI-systems beneficial. In other words, the laggard in the all-important wisdom race gained significant momentum in 2015! Let’s do all we can to make future top news stories be about wisdom winning the race, because then we all win.

1,500 light years away, in the direction of Cygnus, lies a star that probably doesn’t host an advanced civilization.

Glancing up at countless other barren stars, you might think this was a non-story. In fact, it’s big news.

In September, astronomers from the Kepler mission published a description of the star (officially designated KIC 8462852, unofficially “Tabby's Star”, after Tabetha Boyajian, the lead author of the paper). It’s around the same size as the sun, but with a bizarre “light curve”—variations in the intensity of the light received from the star. A planet the size of Jupiter, passing in front of such a star, might be expected to dim its light about 1%. This star’s light has been observed to drop 22% in asymmetric and aperiodic dimming events unlike anything else seen in the Kepler mission.

Possible explanations—none completely satisfactory—include a swarm of disintegrating comets, or a disk of matter surrounding the star (which looks far too old to retain such a disk). Jason Wright, an astronomer at Penn State University who was consulted by Boyajian about the problem, proposed an unlikely but intriguing possibility: that the dimming effect is caused by a swarm of Dyson Spheres—hypothetical megastructures that advanced civilizations might build to capture energy from their stars.

There are three reasons why all this is important.

First, something interesting is happening around Tabby’s star. Even if it’s not a megastructure, investigating it will surely increase our knowledge of stars, the formation of planets, or both.

Second, the anomaly was not discovered by astronomers. It was flagged up by “citizen scientists” from the Planet Hunters project, scanning the Kepler data for signs of unknown extrasolar planets. This is a significant development in 21st-century science: its gradual broadening beyond academia, research institutions and corporations to include the general public. Open data has allowed ordinary people to sample the immense harvest of data collected by instruments like the Kepler telescope. Distributed computing enables them to use their personal computer power to analyse that data. And programs such as Planet Hunters invite them to use their critical faculties to find interesting patterns. We are witnessing the early steps of a revolution in the scientific process: the growth of a planet-wide network of specialists, laypeople, and computers, collaborating to create scientific knowledge.

Third, the mere fact that astronomers can investigate a specific planet as a candidate for intelligent life illustrates how the Kepler mission has transformed astrobiology—from a heroic but marginal pursuit into a popular and rapidly maturing science. Seven years ago, many believed that potentially habitable planets were vanishingly rare. Today, to suggest that there are billions—in our galaxy alone—is a conservative estimate. And more and more evidence—such as the ubiquity of organic molecules in environments beyond our solar system—suggests that life may bloom on some of these planets.

Intelligent life, though, remains a great unknown. We know that it has arisen once in at least 3.5 billion years of evolution on Earth. But extrapolating from Earth to the universe is guesswork.

Yet after Kepler, theories about civilizations beyond Earth are no longer stabs in the dark. Now, as Tabby’s Star shows us, the scope for serious science has expanded enormously. Astronomers and committed non-scientists can study large and growing bodies of data for interesting patterns. When they find them, they can focus the wide resources of modern astronomy—from radio searches to optical spectroscopy to computational modeling—on individual candidate planets.

That means that this century, we may finally have a serious chance of resolving Fermi’s Paradox: Where is everybody?

Our planet is growing itself a brain. That process is accelerating, and the project will determine the future of humans and many other species.

The major Internet and space technology corporations, among others, have confirmed multi-billion-dollar investments to bring low-cost broadband Internet to every square meter of Earth's surface within ten years. They are building the railway tracks and freeways of the 21st century—but at global scale, and with breathtaking speed.

Five billion human minds are therefore about to come online, mostly via sub-$50 smartphones. And unlike the two billion who preceded them, their first experience of the Internet may not be clunky text, but high-resolution video and a fast connection to whatever grabs their imagination.

This is a social experiment without historical precedent. Most of us built our Internet habits on top of years of exposure to newspapers, books, radio and TV. Many of those soon to come online are currently illiterate. Who is going to win their attention and to what consequence? Local language versions of social media, wikipedia, Porn? Video games? Marketing come-ons? Government propaganda? Addictive distractions? Free education? Conversations with mentors in other countries empowered by real-time machine translation?

It's certainly possible to imagine a beautiful scenario in which: For the first time in history every human can have free access to the world's greatest teachers in their own language; people discover the tools and ideas to escape poverty and bigotry; growing transparency forces better behavior from governments and corporations; the world starts to gain the benefit of billions of new minds able to contribute to our shared future; global interconnection begins to trump tribal thinking.

But for that to have even a chance of happening, we need to get ready to engage in the mother of all attention wars. Every global company, every government and every ideology has skin in this game. It could play out in many different ways, some of them ugly.

What's unique and significant is that we have a roadmap. It's now clear that we will not need to physically wire the planet. Satellites, possibly aided by drones and balloons, are about to get the job done a lot faster. We can therefore be certain that a massive transformation is about to hit. We better get ready.

The most accessed parts of the Internet focus on new news and old news via search engines and social network news about shopping, pets, and humans—especially sportful and celebrity humans. What is the distinction between popularity and enduring importance?

In remote indigenous peoples (300 million strong, including Kawahiva, Angu, Sentineli) and our primate relatives, the distinction seems small. In contrast, in our hyper-civilization, the importance of survival has been decoupled from popularity. Our ancient starvation for sugar and fat has morphed today into nearly limitless ad libitum cardio-challenging doughnuts and steaks. Our instincts to reproduce can now be diverted into a wide variety of diversions. Practice for the hunt with rocks and spears has became inflated to 514 stadiums holding 40,000 to 220,000 spectators and up to 4.8 billion viewers via electronics. Mild analgesic herbal medicines have become powerfully pure and addictive. Running toward (or away from) a predator-prey encounter has transformed into a market for massive and speedy cars, killing 1.2 million people per year (roughly equal to all humans alive 10,000 years ago).

Our Darwinian drive to improve our survival relative to other species now includes augmentations that would be baffling to our ancestors—dodging asteroids via mars colonies and handheld neural prosthetic supercomputers with two video cameras.

The new news is that Greenpeace, KMP, and MASIPAG are accused of “crimes against humanity” for blocking (including vandalizing safety testing experiments), from 2002 to 2016, golden rice, which could save a million souls per year from vitamin A deficiency.

The old news, again, this year (courtesy of the national academies of the US, UK and China) is that, after forty years, we still haven’t reached a consensus on whether we want embryo (germline) augmentation. But this is likely a moot point since genetic and non-genetic adult augmentation represents hundred-fold larger markets and much faster potential return on enhancement—weeks rather than decades—web-warp-drive speed vs. human generation speed.

As with ancient (DNA) evolution, so too with new techno-cultural (r)evolution, even a fractional percent advantage grows exponentially (like compounded interest) resulting in a swift and complete displacement of the old.

We seek news of aging reversal and nootropics—memory and cognitive enhancers. We hunt down ways to get ahead of the FDA-EMA-CFDA curve, even risking the very youth and cognition that we seek to extend. Loopholes in the global regulatory fabric include "natural" products, medical tourism, "practice of medicine" (including surgical procedures and stem cell therapies).

Our ability to prioritize and process the news is in an autocatalytic, positive feedback loop in which we extend our brain both biologically and electronically. Surgery could extend our brain capacity from 1.2 kg to 50 kg (routine head loads of the Sherpas of Nepal). The rate of growth of neural systems could be as fast as the doubling time of human cells (about one day) with differentiation from generic stem cells to complex neural nets recently engineered to occur in four days.

With sufficiently intimate proximity of two or more kg-scale brains, the possibility of mind-backups might be closer than via cloning (which lacks neural copying) or via computer simulation (which requires deeper understanding than mere bio-copying and has a million-fold energy inefficiency relative to brains).

The news is that we can measure and manipulate human neural development and activity with the exponentially improving “innovative neurotechnologies” (the last two letters of the BRAIN project acronym). If (when) these augmentations begin to seriously help us process information, that would be mind-boggling and important news.

Ignaz Semmelweis changed our world when, back in 1847, he decided to start washing his hands after he performed an autopsy and before he delivered a baby. When Semmelweis worked in a Viennese obstetric hospital, the germ theory of disease and the concept of “infection” were unknown. Postpartum infections due to “childbed fever” killed a high percentage of women who gave birth in hospitals. Semmelweis knew that there was something in and around dead bodies that had the potential to cause disease, and so he decided to follow the practice of midwives and wash his before delivering a baby. Fewer mothers died, and Semmelweis knew he was onto something. During his lifetime, his innovation was rejected by fellow male physicians, but within decades, evidence from doctors and scientists in other parts of Europe soon provided incontrovertible evidence that he was right. Small organisms like bacteria caused disease, and taking simple precautions like hand washing could lower disease risk.

Thanks to Semmelweis and his intellectual descendants, we follow a range of routines from water boiling and avoiding tropical ice cubes to near-fanatical levels of hand sanitizing, in order to reduce the chances of getting sick because of the nasty bugs in our environment.

We have known for a long time that our bodies harbor lots of “normal flora,” but until about a decade ago, few people studied them. We focused on Semmelweis’s disease-causing bacteria, which we cultured on petri dishes so that we could identify and kill them. The rest of our microbial residents were thought to be pretty much harmless baggage and were ignored.

The introduction of new methods of identifying normal, diverse communities of organisms from DNA alone (including such innovations as high-throughput DNA sequencing) changed all that, and we began to come to realize the magnitude of what we had been missing. The world of critters living in and on us was soon discovered to be vast and complex one, and it mattered.

Since 2008, when the Human Microbiome Project officially started, hundreds of collaborating scientists have started to bring to light the nature and effects of the billions of bacteria that are part of our normal healthy bodies. There isn’t one human microbiome, there are many: There is a microbiome in our hair, one up our nostrils, another in our vaginas, several lavishly differentiated on the vast real estate of our skin, and a veritable treasure trove in our gut, thanks to diligent subcontractors in the esophagus, stomach, and colon.

This great menagerie undergoes changes as we age, so that some of the bacteria that were common and apparently harmless when we were young start to bother us when we’re old, and vice versa. The taxonomic diversity and census of our resident bacteria are more than just subjects of scientific curiosity; they matter greatly to our health. The normal bacteria on our skin, for instance, are essential to maintaining the integrity of the skin’s barrier functions. Many diseases, from psoriasis to obesity, inflammatory bowel disease, some cancers, and even cardiovascular disease, are associated with shifts in our microbiota.

While it’s too early to tell if the changing bacteria are the cause or the result of these problems, the discovery of robust associations between bacterial profiles and disease states opens the door for new treatments and targeted preventive measures. The body’s microbiota also affects and is affected by the body’s epigenome, the chemical factors influencing gene expression. Thus, the bugs on us and in us are controlling the normal action of genes in the cells of our bodies, and changes in the proportions or overall numbers of bacterial affect how our cells work and respond to stress.

Let’s stop thinking about our bodies as temples of sinew and cerebrum, and instead as evolving and sloshing ecosystems full of bacteria, which are regulating our health in more ways than we could ever imagine. As we learn more about our single-celled companions in the coming years, we will take probiotics for curing acute and chronic diseases, we’ll undertake affirmative action to maintain diversity of our gut microflora as we age, and we’ll receive prescriptions for increasingly narrow-spectrum antibiotics to exterminate only the nastiest of the nasties when we have a serious acute infection. Hand sanitizers and colon cleansing will probably be with us for some time, but it’s best just to get used to it now: Bugs R us.

Searching for extraterrestrial intelligence (SETI) has for decades been a “fringe” endeavor. But it’s moving towards the mainstream. In 2015, it gained a big boost from the launch of Breakthrough Listen—a ten-year commitment by the Russian investor Yuri Milner to scan the sky in a far more comprehensive and sustained fashion than ever before.

It’s a gamble: even optimists rate the probability at only a few percent. And of course radio transmission is only one channel whereby aliens might reveal themselves. But the stakes are high. A manifestly artificial signal—even if we couldn’t decode it—would convey the momentous message that “intelligence” had emerged elsewhere in the cosmos.

These searches are more strongly motivated than they were in earlier decades. The Kepler Spacecraft, surely one of the most cost-effective and inspirational projects in NASA’s history, has revealed that most stars in the Galaxy are orbited by retinues of planets. There are literally billions of them in our Milky Way galaxy with the size and temperature of our Earth.

But would these planets have developed biospheres? Or is our Earth unique, while all others are sterile and lifeless? Despite all we know about life’s evolution, its actual origin—the transition from complex molecules to the first replicating and metabolizing systems that we’d deem to be “alive”—has remained a mystery, and relegated to the “too difficult box.” But it is now being addressed by top-ranking scientists. We may soon know whether life’s emergence was a “fluke,” or whether it’s near-inevitable in the kind of “chemical soup” expected on any planet resembling the young Earth—and also whether the DNA/RNA basis of terrestrial life is highly special, or just one of several possibilities.

In seeking other biospheres, clues will surely come from high-resolution spectra, using the James Webb Space Telescope, and the next generation of 30+metre ground-based telescopes that will come on line in the 2020s.

Conjectures about advanced alien life are of course far more shaky than those about simple life. We know, at least in outline, the evolutionary steps whereby nearly 4 billion years of Darwinian evolution led to the biosphere of which we humans are a part. But billions of years lie ahead. I would argue that our remote, posthuman descendents will not be “organic” or biological; and they will not remain on the planet where their biological precursors lived. And this offers clues to the planning SETI searches.

Why is this? It’s because post-human evolution will be spearheaded by super-intelligent (and super-capable) machines. There are chemical and metabolic limits to the size and processing power of “wet” organic brains. But no such limits constrain electronic computers (still less, perhaps, quantum computers); for these, the potential for further development could be as dramatic as the evolution from monocellular organisms to humans. So, by any definition of “thinking,” the amount and intensity that’s done by organic human-type brains will be utterly swamped by the cerebrations of AI. Moreover, the Earth’s biosphere is not essential—indeed, it’s far from an optimal environment—for inorganic AI. Interplanetary space will be the preferred arena where robotic fabricators will have the grandest scope for construction, and where non-biological “brains” may develop insights as far beyond our imaginings as string theory is for a mouse.

This scenario implies that, even if life had originated only on Earth, it need not remain a trivial feature of the cosmos: humans may be closer to the beginning than to the end of a process whereby ever more complex intelligence spreads through the Galaxy. But in that case there would, of course, be no “ET” at the present time.

Suppose, however, that there are other biospheres where life began, and evolved along a similar track to what happened on Earth. Even then, it’s highly unlikely that the key stages would be synchronized. A planet where it lagged significantly behind what has happened on Earth would plainly reveal no evidence of ET. But on a planet around a star older than the Sun, life could have had a head start of a billion years—and already transitioned to the futuristic post-human scenario.

The history of human technological civilization is measured in centuries—and it may be only one or two more centuries before humans are overtaken or transcended by inorganic intelligence, which will then persist and continue to evolve for billions of years. This suggests that if we were to detect ET, it would be far more likely to be inorganic. We would be most unlikely to “catch” it in the brief sliver of time when it took organic form. A generic feature of these scenarios is that “organic” human-level intelligence is just a brief prelude before the machines take over.

It makes sense to focus searches first on Earth-like planets orbiting long-lived stars (the “look first under the lamp-post” strategy). But science fiction authors remind us that there are more exotic alternatives. In particular, the habit of referring to “alien civilizations” may be too anthropocentric—ET could be more like a single “mind.”

Breakthrough Listen will carry out the world’s deepest and broadest search for extraterrestrial technological life. The project involves using radio dishes at Green Bank and at Parkes—and hopefully others including the Arecibo Observatory—to search for non-natural radio transmissions using advanced signal processing equipment developed by a team based at UC Berkeley. Moreover, the advent of social media and citizen science will enable a global community of enthusiasts to download data and participate in this cosmic quest.

Let’s hope that Yuri Milner’s private philanthropy will one day be supplemented by public funding, I’d guess that millions watching Star Wars would be happy if some of the tax revenues from that movie were hypothecated for SETI.

But in pursuing these searches we should remember two maxims, both oft quoted by Carl Sagan. First, “extraordinary claims require extraordinary evidence,” and second, “absence of evidence isn’t evidence of absence.”

Some of the interesting discoveries and observations of the last year include a new species of human, New Horizons’ observations of dwarf planets including Pluto that demonstrated Pluto was more geologically active than anticipated, more accurate data of species loss indicating a track toward a sixth extinction, and a careful measurement of the timing of the impact that triggered the K-Pg extinction and enhanced Deccan Traps volcanic activity that indicates they occurred at essentially the same time—suggesting volcanic activity and the impact might both have contributed to species loss 66 million years ago. But news in science is usually the product of many years of effort, even when it appears to be a sudden revolutionary discovery, and the headlines of any given year are not necessarily representative of what is most significant.

So I’m going to answer a slightly different question, which is what advances I expect we’ll hear about in the coming decade, bearing in mind that the most common stories concern news that in some global sense hasn’t changed all that much. Crisp clean events and many important discoveries are news, but for only a short time. True breakthroughs become part of the culture. General relativity was news in 1915 and the bending of light was news in 1919. Yet although general relativity factors into news today, the theory itself is no longer news. Quantum mechanics stays in the news, but only because people don’t want to believe it, so incremental verifications are treated as newsworthy.

So instead of saying more about the important discoveries of the last year, I’ll give a few examples of scientific advances that I expect we might hear about in the next few. The first is the type I think we won’t really solve, but we will have marginal incremental developments, so it will stay news. The second is a type where we will make advances but the news won’t necessarily reflect the most important implications. The third might be a true discovery or a breakthrough that makes it largely solved, like the Higgs boson discovery that was big news in 2012 but—though exciting and an important guidepost for the future of particle physics—is no longer news today.

The first type of discovery includes a better understanding about what constitutes life, or at least life as we know it. We will learn more about the chemical composition of stuff in the Solar System and perhaps where the elements of life as we know it arose. We might learn more about the chemistry or at least some physical properties of planets in other solar systems, and perhaps deduce more about where life—even if not necessarily complex life—might arise. We will probably also probe the fossil record in greater detail as new chemical and physical methods allow us to probe the Earth’s history more. All of this will stay news since we won’t know how life arose for a long time to come, but small pieces of the puzzle will continue to emerge.

Artificial intelligence and robotics too will show many new developments. But this is probably advances in the second category since a lot of the real news about the role of automation will occur behind the scenes, where technology will make some tasks we already do simpler or more effective or where technology will replace workers and reduce or at the very least dramatically change the nature of employment. We’ll read about drones and medical robotics and advances in AI but those factory robots won’t be big news, except to the families who find themselves on unemployment lines and perhaps for a few days on the business pages.

Hopefully the third category will include discoveries that tell us more about the fundamental nature of dark matter. Dark matter is the matter that carries five times the energy of ordinary matter, and that interacts with gravity, but very little or not at all with light. Current experiments that are already in the news look for dark matter in many different ways. Some of these like Xenon1T and Lux-Zeplin are huge containers of material like xenon deep underground that might detect a tiny recoil of a dark matter particle that passes through. Also possible is that dark matter annihilates with itself when two dark matter particles get together and turn into ordinary matter such as photons.

But there are also less conventional searches that might tell us more about the nature of dark matter that rely on comparing simulations of how structures like galaxies form from dark matter collapse to actual data exploring the distribution of stars or other matter in galaxies. These more detailed observations of the role of dark matter might reveal some interesting aspects of how dark matter interacts. Perhaps dark matter has interactions or forces that familiar matter doesn’t experience—just like dark matter doesn’t experience forces like electromagnetism of the visible world.

If such properties of dark matter are found or if a dark matter particle is really discovered, scientists will continue to try to learn more about its properties and the implications for cosmology and astrophysics. But that will be a long detailed slog from the perspective of outsiders. The true sign that dark matter searches have succeeded will be that the discovery will be taken for granted and will afterward cease to be news.

In 2015, we crossed the threshold of the first million people who had their genomes sequenced. Beyond that, based on the fast pace of progress in sequencing technology, it is projected that we’ll hit 1 billion people sequenced by 2025. That seems formidable, but quite likely given that the velocity of DNA reading innovation has exceeded Moore’s Law. However, the big problem we have is not amassing billions of people’s genome sequences. It is how to understand the significance of each of the 6 billion letters that comprise a human genome.

About 98.5% of our genome is not made of genes, so it doesn’t directly code for proteins. But most of this non-coding portion of the genome influences, in one way or another, how genes function. While it’s relatively straightforward to understand the tiny portion of the genome—genes—that code for proteins, the non-coding elements are far more elusive.

So the biggest breakthrough in genomics—Science Magazine’s 2015 Breakthrough of the Year—is the ability to edit a genome via so-called CRISPR technology with remarkable precision and efficiency. While we’ve had genome editing technologies for several years, including zinc finger nucleases and TALENS, they were not straightforward to use or could achieve a high rate of successful editing in the cells that were exposed. The precision problem also extended to the need to avoid editing in unintended portions of the genome, so called off-target effects. Enter CRISPR and everything has quickly changed.

Many genome editing clinical trials are now underway or will soon be to treat medical conditions for which treatment or a cure has proven remarkably challenging. These include sickle cell disease, thalassemia, hemophilia, HIV, and some very rare metabolic diseases. Indeed, the first person whose life was saved was a young girl with leukemia who had failed all therapies attempted until she had her T cells genome edited (using TALENS) with a very successful response. George Church and his colleagues at Harvard were able to edit 62 genes of the pig’s genome to make it immunologically inert, such that the whole idea of transplanting an animal’s organ into humans—xenotransplantation—has been resurrected. A number of biotech and pharma companies (Vertex, Bayer, Celgene and Novartis), have recently partnered with the editing company startups (CRISPR Therapeutics, Editas Medicine, Intellia Therapeutics, Caribou Biosciences) to rev up clinical programs.

But the biggest contribution of genome editing, and specifically with CRISPR, is to catapult the field of functional genomics forward. Not understanding the biology of the DNA letters is the biggest limitation of our knowledge base in the field. So many interesting DNA sequence variant “hits” have been discovered but overshadowed by uncertainty. Determining functional effects of the VUS—variants of unknown significance—has moved as a very sluggish pace, with too much of our understanding of genomics based on population studies rather than on pinpointing the biology and potential change in function due to an altered (compared with the reference genome) DNA letter.

Now we’ve recently seen how we can systematically delete genes to find out which are essential for life. From that we learned that only about 1600 (8%) of the nearly 19,000 human genes are truly essential. All of the known genes implicated in causing or contributing to cancer can be edited, and indeed that systematic assessment is well underway. We have just learned how important the 3-D structure of DNA is for cancer vulnerability by using CRISPR to edit out a particular genomic domain. What’s more is that we can now generate a person’s cells of interest (from their blood cells, via induced pluripotent stem cells)—be make heart, liver, brain, or whatever the organ/tissue of interest. When this is combined with CRISPR editing, it becomes a remarkably powerful tool that takes functional genomics to an unprecedented level.

What once was considered the “dark matter” of the genome is about to get illuminated. The greatest contribution of genome editing will ultimately be to understand the 6 billion letters that comprise our genome.

In neuroscience, few single discoveries have the ability to stay news for long. However, in the aggregate, all lead to the emergence of perhaps the greatest developing news story: the widespread understanding that human thought and behavior are the products of biological processes. There is no ghost in the machine. In the public sphere, this understanding is dawning.

Consider the recent sea change in public opinion on homosexuality—namely the growing consensus that sexual orientation is not a choice. This transformation suggests that the scale is tipping from ancient intuition to an appreciation of biology with its inherent constraints and promises.

Every year, neuroscience reveals the anatomical and functional brain differences associated with expressing a given trait or tendency, whether psychopathy, altruism, extroversion, or conscientiousness. Researchers electrically stimulating one brain area cause a patient to experience a strong surge of motivation. Zapping a different area causes another patient to become less self-aware. Disease can disorient a patient's moral compass or create illusions of agency. Environmental influences, from what we eat to who we see, provide inputs that interact with and shape our neural activity—the activity that instantiates all our thoughts, feelings, and actions. Finding by finding, the ghost in the machine is being unmasked as a native biological system—like a drawn-out ending of a scientific Scooby Doo.

Lest we think science has effectively communicated the biological basis of human behavior, consider this: It is one thing to convince people that sexual orientation is not a choice. It is quite another to convince people that the dichotomy of biology versus choice made no sense at the start. Who, but the unmasked biological system, is doing the “choosing”? Choosing whether to take medication is as much a biological phenomenon as the disease to be medicated. Choice is simply a fanciful shorthand for biological processes we do not yet apprehend. When we have communicated that—when references to choice occupy the same rhetorical space as the four humors—we will be poised to realize public policy in harmony with a scientific understanding of the mind.

At the close of the year 2015, in close succession, two rockets left the ground, crossed the Karman line (at 100 km altitude) into space, and return intact under their own power to a soft landing on the surface of the earth. In the space business, new rockets are launched at regular intervals, but the now-imminent launch of a used rocket is important news.

In December of 1966, Theodore B. Taylor complained that the high cost of sending anything into even low Earth orbit was “roughly equivalent to using jet transport planes to carry freight from, let us say, Madrid to Moscow, making one flight every few weeks, throwing away each aircraft after each flight, and including the entire construction and operation costs of several major airports in the cost of the flights!”

The now-abandoned Space Shuttle was a reusable spacecraft but failed to reduce launch costs and violated one of the cardinal rules of transport: separate the passengers from the freight. Some day, we will look back and recognize one of the other roadblocks to an efficient launch system: separating the propellant from the fuel.

There is no reason the source of reaction mass (propellant) has to be the same as the source of energy (fuel). Burning a near-explosive mix of chemicals makes the process inherently dangerous and places a hard limit on specific impulse (ISP), a measure of how much acceleration can be derived from a given amount of propellant/fuel. It is also the reason that the original objective of military rocketry—“to make the target more dangerous than the launch site”—took so long to achieve.

The launch business has been crippled, so far, by a vicious circle that has limited the market to expensive payloads—astronauts, military satellites, communication satellites, and deep space probes—consigned by customers who can afford to throw the launch vehicle away after a single use. Reusable rockets are the best hope of breaking this cycle and moving forward on a path leading to low-cost, high-duty-cycle launch systems where the vehicle carries inert propellant, and the energy source remains on the ground.

All the advances in autonomous control, combustion engineering, and computational fluid dynamics that allowed these two rockets to make a controlled descent, after only a handful of attempts, are exactly what will be needed to develop a new generation of launch vehicles that leave chemical combustion behind to ascend on a pulsed energy beam.

Scientists and the media are establishing new ways of looking at who is responsible for anthropogenic climate change. This expanded view of responsibility is some of the most important news of our time, because whomever we see as causing the problem informs whom we see as obligated to help fix it.

The earliest phases of climate responsibility focused on greenhouse gas emissions by country, and highlighted differences between developed and developing nations (a distinction that has become less valuable as China and India have become two of the top three national emitters). Then, in the first decade of the twenty-first century, the focus, at least in the U.S., narrowed in on individual consumers.

However, in a very short time, the twenty-first century’s second decade has brought corporate producers into the spotlight, not only for their role in greenhouse gas emissions but also for their coordinated efforts to mislead the public about the science of climate change and prevent political action.

Although we have traditionally held producers responsible for pollutants, as in the case of hazardous waste, a debate followed about whether it was fair to shift the burden of responsibility for greenhouse gas emissions from demand to supply. New research revealing how some corporate fossil fuel companies responded to climate science has placed a greater burden on the producer.

Since the late 1980s, when the risks of climate change began to be clear, some corporations systematically funded efforts to deny and reject climate science and worked to ensure the future of fossil fuels. Producers influenced public beliefs and preferences.

One reason for the recent research into corporate influence is the increasing number of disciplines (and interdisciplines) involved in climate research. While psychologists were some of the first to conduct headline-generating climate-related social science (which helps explain the focus on individual responsibility and preferences), researchers from other disciplines like sociology and history of science joined in and are documenting the role of corporations and a complicit media in the failure to act on climate change. They are part of the reason for our expanded view of responsibility.

The mounting evidence for producer culpability in climate change has happened relatively quickly, but its timing remains embarrassing. Over the last couple decades of the climate wars, scientists have been blamed in so many ways for the lack of action on climate change. They have been accused of being bad communicators, of emphasizing uncertainty, and of depressing and scaring people. I find none of these lines of argument particularly convincing.

But the failure of researchers and the media to, until recently, neither see nor document the industry legerdemain as partly responsible for the stalemate over climate represents their (our) biggest failure on climate action. We might be able to blame corporate influence over politics and the media for the public opinion divide on climate change, but that does little to explain how researchers and journalists overlooked the role of corporations for so long.

Now that we have seen the important role of industry in climate change, hopefully we do not allow it to regress into our collective blind spot.

The publication in 2015 of a new paper from Leicester University, "The Anthropocene Biosphere," provides more evidence that the changes wrought upon the climate by human civilization are set to bring down a sixth mass extinction. According to the paper’s co-author, geologist Peter Haff, we have already entered a period of fundamental changes to the planetary system that may continue to change the world beyond our imagination, a fear echoed by Adelaide University’s John Long in the New Scientist last month. All of us can provide anecdotal evidence of the shifts that are changing our environment. Today I received a call from a friend in Engadin, Switzerland, where Nietzsche wrote Thus Spoke Zarathustra. In late December, at an altitude of 2,000 metres, there is no snow. In Hyde Park, the daffodils are in bloom.

As the artist, environmentalist, and political activist Gustav Metzger has been saying for many years, it is no longer enough just to talk about ecology, we need to create calls to action. We must consider the potential for individual and collective agency to effect changes in our behavior, and to develop adaptive strategies for the Anthropocene age. To quote Gustav Metzger, “we need to take a stand against the ongoing erasure of species, even though there is little chance of ultimate success. It is our privilege and our duty to be at the forefront of the struggle.” We must fight against the disappearances of species, languages and entire cultures. We must battle against the homogenization of our world. We must understand this news as part of a broader continuum. The French historian Fernand Braudel advocated the “longue durée,” a view of history which relegates the historical importance of “news events” beneath slow shifts, and occasional crises, in the grand underlying structures of human civilisation. Extinction is a phenomenon that belongs to the “longue durée” of the Anthropocene, the symptoms of which we are now beginning to experience as news. By connecting the news to the “longue durée” we can formulate strategies to transform our future and avert the most catastrophic scenarios of extinction. By understanding the news we can act upon it.

Art is one means by which we reimagine existing paradigms to accommodate new discoveries, the thread linking news events to the “longue durée.” Art is also a means of pooling knowledge and it is, like literature, news that STAYS news. It is the means by which we adjust existing paradigms to accommodate new discoveries, the thread connecting the now to the past and to the future.

When Shelley stated that “poets are the unacknowledged legislators of the world” he meant something like this: that writers and artists have the capacity to reimagine news in ways that change the way that we perceive the world, the way we think and act.

Among my great inspirations is Félix Fénéon, a fin-de-siècle French editor (and the first publisher of James Joyce in France), art critic (he discovered and popularised the work of Georges Seurat), and anarchist (put on trial, he escaped prosecution after famously directing a series of barbs at the prosecutor and judge, to the jury’s great entertainment). Félix Fénéon, was a master of transformation. He transformed the news into world literature via his series of prose poems: In 1906 he was the anonymous author of a series of three-line news items published in the Paris newspaper Le Matin, which have since become famous. These brief reports adapt stories of contemporary murder and misery into prose poems that will last forever.

Through their adaptation of the literary and rhetorical devices of rhythm, prosody, and hard-edged juxtaposition, Fénéon transforms minor news items—by themselves petty, random, isolated—into history.

Lawrence Durrell’s Alexandria Quartet transformed the Copernican breakthroughs of Einstein and Freud into fiction. By translating events which are ephemeral and local in their initial impact into that which is universal and enduring, we can make news into culture. John Dos Passos gave lasting form to events that seemed characterised by their fleeting immediacy. In his U.S.A. trilogy Dos Passos pioneered new styles of writing that sought to capture the experience of living in a society overwhelmed by the proliferation of print media, television and advertising. In his “newsreel” sections, the author collages newspaper clippings and popular song lyrics; elsewhere he pursues his experiments in what he called the “camera eye,” a stream of consciousness technique that attempts to replicate the unfiltered receptivity of the camera, which makes no distinction between what is important and what is not. Later this material is transformed into stories. The film-maker Adam Curtis told me that the U.S.A. trilogy identifies “the great dialectic of our time, which is between individual experience and how those fragments get turned into stories... It’s like when you live through an experience you have no idea what it means. It’s only later, when you go home, that you reassemble those fragments into a story. And that’s what individuals do, and it’s what societies do. It’s what the great novelists of the nineteenth century, like Tolstoy, wrote about. They wrote about that tension between how an individual tells the story of an event themselves, out of fragments, and how society then does it.”

The Lebanese-American poet, painter, novelist, urbanist, architect and activist Etel Adnan speaks about the process of transformation as the “beautiful combination of a substratum that is permanent and something that changes on top. There is a notion of continuity in transformation.” In Etel Adnan’s telling, transformation describes the relationship between the “longue durée” of history, news events in the present, and action that can transform the future. Adnan shows us how dialogue can produce new strategies that can preserve difference, can help to act against extinction, while also acknowledging that change is inevitable. If we are to develop radical new strategies to address one of the most important issue of our time, then it is urgent now that we go beyond the fear of pooling knowledge between disciplines. If we do not pool knowledge, then the news is just news: each new year will bring reports of another dead language, another species lost. While writing this text I received an email in the form of a poem from Etel Adnan which expresses this beautifully:

Where do the news go?

News go where angels go
News go into the waste-baskets of foreign embassies
News go in the cosmic garbage that the universe has become
News go (unfortunately) into our heads

The topic itself is not new. For decades, there have been rumors about famous historical scientists like Newton, Kepler, and Mendel. The charge was that their research results were too good to be true. They must have faked the data, or at least prettied it up a bit. But Newton, Kepler, and Mendel nonetheless retained their seats in the Science Hall of Fame. The usual reaction of those who heard the rumors was a shrug. So what? They were right, weren't they?

What's new is that nowadays everyone seems to be doing it, and they're not always right. In fact, according to John Ioannidis, they're not even right most of the time.

John Ioannidis is the author of a paper titled "Why Most Published Research Findings Are False," which appeared in a medical journal in 2005. Nowadays this paper is described as "seminal" and "famous," but at first it received little attention outside the field of medicine, and even medical researchers didn't seem to be losing any sleep over it.

Then people in my own field, psychology, began to voice similar doubts. In 2011, the journal Psychological Science published a paper titled "False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant." In 2012, the same journal published a paper on "the prevalence of questionable research practices." In an anonymous survey of more than 2000 psychologists, 53 percent admitted that they had failed to report all of a study's dependent measures, 38 percent had decided to exclude data after calculating the effect it would have on the outcome, and 16 percent had stopped collecting data earlier than planned because they had gotten the results they were looking for.

The final punch landed in August, 2015. The news was published first in the journal Science and quickly announced to the world by the New York Times, under a title that was surely facetious: "Psychologists welcome analysis casting doubt on their work." The article itself painted a more realistic picture. "The field of psychology sustained a damaging blow," it began. "A new analysis found that only 36 percent of findings from almost 100 studies in the top three psychology journals held up when the original experiments were rigorously redone." On average, effects found in the replications were only half the magnitude of those reported in the original publications.

Why have things gone so badly awry in psychological and medical research? And what can be done to put them right again?

I think there are two reasons for the decline of truth and the rise of truthiness in scientific research. First, research is no longer something people do for fun, because they're curious. It has become something that people are required to do, if they want a career in the academic world. Whether they enjoy it or not, whether they are good at it or not, they've got to turn out papers every few months or their career is down the tubes. The rewards for publishing have become too great, relative to the rewards for doing other things, such as teaching. People are doing research for the wrong reasons: not to satisfy their curiosity but to satisfy their ambitions.

There are too many journals publishing too many papers. Most of what's in them is useless, boring, or wrong.

The solution is to stop rewarding people on the basis of how much they publish. Surely the tenure committees at great universities could come up with other criteria on which to base their decisions!

The second thing that has gone awry is the vetting of research papers. Most journals send out submitted manuscripts for review. The reviewers are unpaid experts in the same field, who are expected to read the manuscript carefully, make judgments about the importance of the results and the validity of the procedures, and put aside any thoughts of how the publication of this paper might affect their own prospects. It's a hard job that has gotten harder over the years, as research has become more specialized and data analysis more complex. I propose that this job should be performed by paid experts—accredited specialists in the analysis of research. Perhaps this could provide an alternative path into academia for people who don't particularly enjoy the nitty-gritty of doing research but who love ferreting out the flaws and virtues in the research of others.

In Woody Allen's movie "Sleeper," set 200 years in the future, a scientist explains that people used to think that wheat germ was healthy and that steak, cream pie, and hot fudge were unhealthy—"precisely the opposite of what we now know to be true." It's a joke that hits too close to home. Bad science gives science a bad name.

Whether wheat germ is or isn't good for people is a minor matter. But whether people believe in scientific research or scoff at it is of crucial importance to the future of our planet and its inhabitants.

One of the biggest challenges facing us is the increasing disparity in wealth and income which has become obvious in American society in the last four decades or so, with all its pernicious effects on societal health. Thomas Piketty's extensively data-backed tour de force,Capital in the Twenty-First Century (2013), gave us two alarming pieces of news about this trend: 1) Inequality is worse than we thought, and 2) it will continue to worsen because of structural reasons inherent in our form of capitalism, unless we do something.

The top 0.1 percent of families in America went from having 7 percent of national wealth in the late 1970s to having about 25 percent now. Over the same period, the income share of the top 1 percent of families has gone from less than 10 percent to more than 20 percent. And lest we think that even if wealth and income are more concentrated America is still the land of opportunity and those born with very little have a good chance to move up in economic class, a depressing number of studies show that according to standard measures of intergenerational mobility, the United States ranks among the least economically mobile of the developed nations.

Piketty shows that an internal feature of capitalism increases inequality: As long as the rate of return on capital (r) is greater than the rate of economic growth (g), wealth will tend to concentrate in a minority, and that the inequality r > g always holds in the long term. And he is not some lone-wolf academic with an eccentric theory of inequality. Scores of well-respected economists have given ringing endorsements to his book's central thesis, including economics Nobel laureates Robert Solow, Joseph Stiglitz, and Paul Krugman. Krugman has written that

Piketty doesn’t just offer invaluable documentation of what is happening, with unmatched historical depth. He also offers what amounts to a unified field theory of inequality, one that integrates economic growth, the distribution of income between capital and labor, and the distribution of wealth and income among individuals into a single frame.

The only solution to this growing problem, it seems, is the redistribution of the wealth concentrating within a tiny elite using instruments such as aggressive progressive taxation (such as exists in some European countries that show a much better distribution of wealth), but the difficulty here is the obvious one that political policymaking is itself greatly affected by the level of inequality. This vicious positive-feedback loop makes things even worse. It is clearly the case now in the United States that not only can the rich hugely influence government policy directly but also that elite forces shape public opinion and affect election outcomes with large-scale propaganda efforts through media they own or control. This double-edged sword attacks and shreds democracy itself.

The resultant political dysfunction makes it difficult to address our most pressing problems—for example, lack of opportunity in education, lack of availability of quality healthcare, man-made climate change, and not least the indecent injustice of inequality itself. I am not sure if there is any way to stop the growth in inequality that we have seen in the last four or five decades anytime soon, but I do believe it is one of the very important things we have learned more about just in the last couple of years. Unfortunately the news is not good.

It now seems inevitable that the decreasing cost and the increasing resolution of brain scanning systems, accompanied by the relentless increase in power of computers, will take us soon to the point where our own thinking might be visible, downloadable and open to the world in new ways.

It was the news that brain scanners are starting to be developed at consumer price levels that has obsessed me this last year.

Through the work of Mary Lou Jepsen, I was introduced to the potential of brain reading devices and that patterns generated while watching a succession of very varied videos would provide the fundamental elements to connect thought to image. A starting point was the work pioneered at Jack Gallant’s Lab at UC Berkeley in 2011 that proved that the patterns of brain activity from MRI scanners, when a subject was viewing an assortment of videos, would enable thoughts to be translated into digital images.

Recording more and more images and corresponding brain patterns boosts the vocabulary in the individual’s visual dictionary of thought. Accuracy greatly increases with the quantity and quality of data and of the decoding algorithms. Jepsen has persuaded me that this is realisable within a decade, within the cost points of consumer electronics, and in a form that appeals to non-techies. Laborious techniques and huge, power-hungry, multi-million-dollar systems based on magnetic fields will be succeeded by optical techniques where the advantages of consumer electronics can really assert themselves; the power of AI algorithms will do the rest. This science-fiction future is not only realisable, but because of enormous potential benefits, will inevitably be realised.

And so, here we are: our thoughts themselves are about to take a leap out of our heads: from our brains to computers, to the Internet and to the world. We are entering the Age of Visible (and Audible) Thought. This will surely affect human life as deeply as any technology our imagination has yet devised or any evolutionary advance.

The essence of who we are is contained in our thoughts and memories, which are about to be opened like tin cans and poured onto a sleeping world. Inexpensive scanners would enable all of us to display our own thoughts and access those of others who do the same. The consequences and ethics of this have barely been considered.

I imagine the pioneers of this research enjoying a heady Oppenheimer cocktail of anticipation and foreboding, of exhilaration and dread, of knowing what fundamental changes and dangers these inventions could create. Our task is to assure they do not feel alone or ignored.

One giant tech company is believed to have already backed off exploring the development of brain reading for Visual Thought, apparently for fear of potentially negative repercussions and controversy around privacy. The emergence of this suite of technologies will have enormous impact on the everyday ways we live and interact and can clearly transform, positively and negatively, our relationships, aspirations, work, creativity, techniques for extracting information.

Those not comfortable swimming in these transparent waters are not going to flourish. Perhaps we will need to create “swimming lessons” to teach us how to be comfortable being open, honest and exposed—that we can be ready to float and navigate in these waters of visible thought.

What else happens in a World of Visible Thought?

One major difference is that as thought becomes closer and closer to action, with shorter feedback loops accelerating change, time scales collapse and the cosy security blanket of a familiar slowness evaporates.

A journey for my grandfather from London to New York shrank from a perilous three weeks to a luxurious three hours for my generation in Concorde. Similarly, plugging thought directly into the material world will all but eliminate the comfort of time lag. If I look outside at the streets, the buildings, the cars, I am just looking at thought turned into matter, the idea in its material form. With 3D printing and robotics, that entire process can become nearly instantaneous.

The last year has witnessed robots building bridges and houses, but these currently work from 3D blueprints. Soon, we will be able to plug in the architect directly and with a little bit of fine tuning, see her latest thoughts printed and assembled into a building that same day. The same goes for film and for music and every other creative process. Barriers between imagination and reality are about to burst open. Do we ignore it or do we get into boat building like Noah? Here comes the flood. ...

When I take a look at John Brockman’s original list of "hot topics" from a quarter of a century ago, I discern a pattern, or throughline. We live at a time of great, perhaps unprecedented, advances in digital technology (hardware/software) and biological (genetic/brain) research and applications. It’s easy to see these changes as wholly or largely positive, though—as a card-carrying member of the pessimists’ society—I can easily point to problematic aspects as well.

But irrespective of how full (or empty) you believe the glass to be, a powerful question emerges: To what extent will our conceptions of what it means to be human change?

History records huge changes in our species over the last 5000 years or so—and presumably pre-history would fill in the picture. But scholars have generally held the view that the fundamental nature of our species—the human genome, so to speak—has remained largely the same for at least 10,000 years and possibly much longer. As Marshall McLuhan argued, technology extends our senses—it does not fundamentally change them. Once one begins to alter human DNA—for example, through CRISPR—or the human nervous system—by inserting mechanical or digital devices—then we are challenging the very definition of what it means to be human. And once one cedes high level decisions to digital creations, or these artificially intelligent entities cease to follow the instructions that have been programmed into them and rewrite their own processes, our species will no longer be dominant on this planet.

In a happy scenario, such changes will take place gradually, even imperceptibly, and they may lead to a more peaceful and even happier planet. But as I read the news of the day, and of the last quarter century, I discern little preparedness on the part of human beings to accept a lesser niche, let alone to follow Neanderthals into obscurity. And so I anticipate the news of tomorrow to highlight human resistance to fundamental alterations in our makeup and, quite possibly, open warfare among old and newly emerging creatures. But there’ll be one difference from times past: rather than looking for insights in the writings of novelists like Aldous Huxley or George Orwell or Anthony Burgess, we’ll be eavesdropping on the conversations among members of the Third Culture.

For most of its history our species has systematically squandered its human capital by spurning the creative potential of half its members. Higher education was withheld from women in just about every place on earth until the twentieth century, with the few who persevered before then considered “unsexed.” It’s only been in the last few decades that the gap has so significantly closed that, at least in the U.S., more bachelor’s degrees have been earned by women than by men since 1982, and, since 2010, women have earned the majority of doctoral degrees. This recent progress only underscores the past’s wasteful neglect of human resources.

Still, the gender gap has stubbornly perpetuated itself in certain academic fields, usually identified as STEM—science, technology, engineering, and mathematics, and this is as true in Europe as in the U.S. A host of explanations have been posed as to the continued male dominance—some only in nervous, hushed voices—as well as recommendations for overcoming the gap. If the under-representation of women in STEM isn’t the result of innate gender differences in interests and/or abilities (this last, of course, being the possibility that can only be whispered), then it’s important for us to overcome it. We’ve got enormously difficult problems to solve, both theoretical and practical, and it’s lunacy not to take advantage of all the willing and able minds that are out there.

Which is why I found an article published this year in Science by Andrei Cimpian and Sarah-Jane Leslie big news. First of all, their data shows that the lingering gender gap shouldn’t be framed in terms of STEM versus non-STEM. There are STEM fields—for example, neuroscience and molecular biology—that have achieved 50% parity in the number of Ph.D.’s earned by men and women in the U.S., and there are non-STEM fields—for example, music theory and composition (15.8%) and philosophy (31.4%)—where the gender gap rivals such STEM fields as physics (18.0%) computer science (18.6%) and mathematics (28.6%). So that’s the first surprise that their research delivers, that it’s not science per se that, for whatever reasons, produces stubborn gender disparity. And this finding in itself somewhat alters the relevance of the various hypotheses offered for the tenacity of the imbalance.

The hypothesis that Leslie and Cimpian tested is one I’ve rarely seen put on the table and surely not in a testable form. They call it the FAB hypothesis—for field-specific ability beliefs. It focuses on the belief as to whether success in a particular field requires pure innate brilliance, the kind of raw intellectual power that can’t be taught and for which no amount of conscientious hard work is a substitute. One could call it the “Good-Will-Hunting quotient,” after the 1997 movie that featured Matt Damon as a janitor at MIT who now and then, in the dead of night, pauses to put down his mop in order to effortlessly solve the difficult problems left scribbled on a blackboard.

In order to test the FAB hypothesis, the researchers sent out queries to practitioners—professors, post-docs, and graduate students—in leading universities in the U.S, probing the extent to which the belief in innate brilliance prevailed in the field. In some fields, success was viewed as more a function of motivation and practice, while in others the Good-Will- Hunting quotient was more highly rated.

And here’s the second surprise: the strength of the FABs in a particular field predicts the percentage of women in that field more accurately than other leading hypotheses, including field-specific variation in work-life balance and reliance on skills for systematizing vs. empathizing. In other words, what Cimpian and Leslie found is that the more that success within a field was seen as a function of sheer intellectual firepower, with words such as “gifted” and “genius” not uncommon, the fewer the women. The FAB hypothesis cut cleanly across the STEM/non-STEM divide.

Cimpian and Leslie are careful to stress that they don’t interpret their findings as indicating that the FAB hypothesis provides the sole factor behind the lingering gender gap, but simply argue that it is operative. And in follow-up studies, they also discuss informal evidence that raises the plausibility of the FAB hypothesis, including the number of fictional male geniuses inhabiting popular culture—from Sherlock Holmes to Dr. House to Will Hunting—compared to the number of female geniuses. The stereotype of the genius is overwhelmingly male. And when, I might add, a female genius is the subject, her femaleness itself becomes the focus as much as, or even more than, her genius. If genius is an aberration, then female genius is viewed as significantly more aberrational, since it’s seen as an aberration of femaleness itself. Given such stereotypes, is it unlikely that fields that highlight innate genius would show lagging female numbers?

The authors were exclusively concerned with academic fields. But there is another area of human creativity in which words like “gifted” and “genius” are not uncommon, and that is the arts—including literature. Here, too, cold, hard, statistics tell a story of persistent gender imbalance. For despite the great number of contemporary women writers, data compiled by VIDA, a women’s literary organization, reveal that the leading American and British literary magazines, the kind whose very attention is the criterion for distinguishing between the important figures and the others, focus their review coverage on books written by men, and commission more men than women to write about them. Might it be that the FAB hypothesis explains this imbalance as well, highlighting Cimpian’s and Leslie’s findings that the problem is not, essentially, one of STEM vs. non-STEM, nor of mathematical vs. verbal skills?

I realize that discussing the FAB hypothesis will be seen as small stuff compared to such big news as, say, the icecaps melting at a faster rate than anticipated. And that is why, in responding to this year’s Edge Question, I first began to write about the icecaps. But perhaps the insignificant measure we assign to the under-estimation of the creative potential of more than half our population is itself a manifestation of the problem. And what could be a greater boon to humanity than increasing the, um, man-power of those making important contributions, not only to science but to our culture at large?

Physicist, University of Vienna; Scientific Director, Institute of Quantum Optics and Quantum Information; President, Austrian Academy of Sciences; Author, Dance of the Photons: From Einstein to Quantum Teleportation

The notion of quantum entanglement, famously called “spooky action at a distance” by Einstein emerges more and more as having deep implications for our understanding of the World. Recent experiments have perfectly verified the fact that quantum correlations between two entangled particles are stronger that any classical, local pre-quantum worldview allows. So, since quantum physics predicts these measurement results for at least eighty years, what’s the deal?

The point is that the predictions of quantum mechanics are independent of the relative arrangement in space and time of the individual measurements. Fully independent of their distance, independent of which is earlier or later etc. One has perfect correlations between all of an entangled system even as these correlations cannot be explained by properties carried by the system before measurement. So quantum mechanics transgresses space and time in a very deep sense. We would be well advised to reconsider the foundations of space and time in a conceptual way.

To be specific, consider an entangled ensemble of systems. This could be two photons, or any number of photons, electrons, atoms, and even of larger systems like atomic clouds at low temperature, or superconducting circuits. We now do measurements individually on those systems. The important point is that, for a maximally entangled state, quantum physics predicts random results for the individual entangled property.

To be specific, for photons this could the polarization. That is, for a maximally entangled state of two or more entangled photons, the polarization observed in the experiment could be anything, horizontal, vertical, any direction linear, right-handed circular, left-handed circular, any elliptical state, again, for the individual photon. Thus, if we do a measurement we observe a random polarization. And this for each individual photon of the entangled ensemble. But a maximally entangle state predicts perfect correlations between the polarizations of all photons making the entangled state up.

To me, the most important message is that the correlations between particles like photons, electrons, or atoms, or larger systems like superconducting circuits are independent of which of the systems are measured first and how large the spatial distance between them is.

At first sight, this might not be surprising. After all, if I measure the heights of peaks of the mountains around me, it also does not matter in which sequence I do the measurements and whether I measure the more distant ones first or the ones closer to each other. The same is true for measurements on entangled quantum systems. However, the important point is that the first measurement on any system entangled with others instantly changes the common quantum state describing all, the subsequent measurement on the next does that again and so on. Until, in the end, all measurement results on all systems entangled with each other, are perfectly correlated.

Moreover, as recent experiments finally prove, we now know definitely that all this cannot be explained by any communication limited by Einstein’s cosmic speed limit—the speed of light. Also, one might think that there is a difference if two measurements are done such that one if after the other in way that a signal could tell the second one what to do as a consequence of the first earlier measurements. Or whether they are arranged at such a distance and done sufficiently simultaneously such that no signal is fast enough.

Thus, it appears that on the level of measurements of properties of members of an entangled ensemble, quantum physics is oblivious to space and time.

It appears that an understanding is possible via the notion of information. Information seen as the possibility of obtaining knowledge. Then quantum entanglement describes a situation where information exists about possible correlations between possible future results of possible future measurements without any information existing for the individual measurements. The latter explains quantum randomness, the first quantum entanglement. And both have significant consequences for our customary notions of causality.

It remains to be seen what the consequences are for our notions of space and time, or space-time for that matter. Space-time itself cannot be above or beyond such considerations. I suggest we need a new deep analysis of space-time, a conceptual analysis maybe analogous to the one done by the Viennese physicist-philosopher Ernst Mach who kicked Newton’s absolute space and absolute time form their throne. The hope is that in the end we will have new physics analogous to Einstein’s new physics in the two theories of relativity.

The biggest news story over the past quarter century—that will continue to underlie all the currents, gyres and eddies of individual sciences going forward—is the democratization of scientific knowledge. The first wave of knowledge diffusion happened centuries ago with the printing press and mass-produced books. The second wave took off after the Second World War with the spread of colleges and universities and the belief that a higher education was a necessary ingredient to being a productive citizen and cultured person. The third wave began a quarter century ago with the Third Culture: "those scientists and other thinkers in the empirical world who, through their work and expository writing, are taking the place of the traditional intellectual in rendering visible the deeper meanings of our lives, redefining who and what we are," in John Brockman’s 1991 description.

A lot has happened in twenty-five years. While some Third Culture products remain topical (AI, human genetics, and cyberspace) and others have faded from consciousness (chaos, fractals, and Gaia), the culture of science as a redefining force endures and expands into the nooks and crannies of society through ever growing avenues of communication, pulling everyone in to participate. A quarter century ago the Third Culture penetrated the public primarily through books and television; to these technologies of knowledge Third Culture apostles spread the gospel through ebooks and audio books, digital books and virtual libraries, blogs and microblogs, podcasts and videocasts, file sharing and video sharing, social networks and forums, MOOCs and remote audio and video courses, virtual classrooms and even virtual universities.

The news is not just the new technologies of knowledge, however, but the acceptance by society’s power brokers that Third Culture products are the drivers of all other cultural products—political, economic, social, and ideological—and the realization of citizens everywhere that they too can be influential agents by absorbing and even mastering scientific knowledge.

This democratization of science changes everything because it means we have unleashed billions of minds to solve problems and create solutions. The triumphs of the physical and biological sciences in the 20th century are now being matched by those in the social and cognitive sciences because, above all else, we have come to understand that human actions more than physical or biological forces will determine the future of our species.

Big data gives business, government, and social scientists access to information never available before. With the right tools of analysis—which are improving exponentially as I write—big data will transform the way we understand the world and the means we use to fix problems. The US and other governments are building the capacity to use big data as a basis for determining best practices; university-based research programs are generating appropriate analytic tools; and various non-profits around the world are linking technology, data, and citizens to enhance the implementation of government programs and services.

Science can now effectively be brought to bear on public policymaking. Yet, important distinctions exist among the key players. One set of actors wants to ensure that public policies are evidence-based, and a second set aims to enable citizens to complain about poor services and to get the services they need. Some are fundamentally concerned with the science and others with voice.

Evidence-based policy has become a mantra in some circles, and increasingly the focus is on assessment of policies once enacted as well as on the ex ante crafting of good policy. Randomized experiments have gained popularity worldwide by bringing scientific rigor into the appraisal of interventions meant to improve well-being. But they are not the only tools in the toolbox. Observational analyses using big data are just as important, particularly where randomization of people and communities is undesirable, infeasible, unethical, inadequate, or all of the above. Political considerations often trump randomization when it comes to the location of hospital facilities, military bases, and schools. Even in very politicized circumstances, new techniques of causal inference from observational data make it possible to learn about the conditions under which different policies are likely to succeed. Indeed, the progress in recent years on generating scientific inferences from observational data has been breathtaking.

Simultaneously, another group of actors are stepping up to the plate to adapt and improve current technologies, data platforms, and analytic advances in the service of citizen voice. Providing individuals with mobile phones to take pictures, send texts and emails, and otherwise document what they see offers citizens a means for reporting on where things are broken and for demanding that they be fixed. It is also a new and important form of quality control over elections, services, and bureaucrats. Reporting leaking gas mains or water hydrants, photographing pot holes and abandoned homes, and naming corrupt officials can lead to significantly improved government responsiveness—and in some places already has, generally as a result of the work of nonprofits, such as Code for America in the US and eGovernments Foundation in India, or of university-based research teams collecting evidence on how government actually functions. One recent success involves discovering and correcting the gap in the distribution and use of food stamps in California.

The amount and kind of data collected from all of us does pose dangers to privacy and misuse. Science and engineering are being mobilized to assure the proper protections are in place, but governments must also convince publics that they are trustworthy in how they use the data that they access. At stake is the promise of better government that draws on scientific analysis of policy and scientific and technological amplifiers of voice.

On the ice-capped heights of Labrador, through winter, snow falls. With the coming of spring, much of it melts. Sometimes more falls than melts, and the ice grows; sometimes more melts than has fallen, and the ice shrinks. It is a delicate balance. The result varies from year to year, by many inches. But let the balance tip ever so slightly, so that amidst much larger fluctuations one inch on average survives, and Earth is transformed. Great glaciers grow, and cover North America in ice.

If corresponding processes in Greenland or Antarctica tip the other way, melting more than is frozen, then oceans will swell, and drown North America's coasts.

Episodes of both sorts have happened repeatedly in Earth's history, on timescales of a few tens of thousands of years. They are probably controlled by small, long-period changes in Earth's orbit. Today we are living in a relatively rare interglacial period, expected to last another fifty thousand years. Notoriously, over the last few decades human activity has tipped the balance toward melting, threatening catastrophe.

These mighty stories derive from systematic trends that can be hard to discern within the tumult of much larger, but ephemeral, noise. The news is not the news.

So it is with the grandest of human stories: the steady increase, powered by science, of our ability to control the physical world. Richard Feynman memorably expressed a related thought

From a long view of the history of mankind, seen from, say, ten thousand years from now, there can be little doubt that the most significant event of the 19th century will be judged as Maxwell's discovery of the laws of electrodynamics.

In that spirit, the most significant event of the 20th century is the discovery of the laws of matter in general. That discovery has three components: the frameworks of relativity and quantum mechanics, and the specific forces laws embodied in our core theory, often called the standard model. For purposes of chemistry and engineering—plausibly, for all practical purposes—we've learned what Nature has on offer.

I venture to guess that the most significant event of the 21st century will be a steady accumulation of new discoveries, based on deeper use of quantum physics, which harness the physical world. In the 21st century we will learn how to harvest energy from the Sun, and to store it efficiently. We will learn how to make much stronger, much lighter materials. We will learn how to make more powerful and more versatile illuminators, sensors, communication devices, and computers.

We know the rules. Aided by our own creations, in a virtuous cycle, we'll learn how to play the game.

Every time you learn something, your brain changes. Children with autism have brains that differ from those that do not. Different types of moral decisions are associated with different patterns of brain activity. And when it comes to spiritual and emotional experience, neural activity varies with the nature of the experience.

In most ways, this is news that shouldn’t be news—at least not this century, nor probably the last. Given what we already know about the brain and its relationship to behavior and experience, these claims don’t tell us anything new. How could it have been otherwise? Any difference in behavior or experience must be accompanied by some change in the infrastructure that implements it, and we already know to look to the brain.

So why do neuroscientific findings of this type still make the news?

In part, it’s because the details might be genuinely newsworthy—perhaps the specific ways in which the brain changes during learning, for example, can tell us something important about how to improve education. But there are two other reasons why neuroscientific findings about the mind might make headlines, and they deserve careful scrutiny.

The first reason comes down to what psychologist Paul Bloom calls “intuitive dualism.” Intuitive dualism is the belief that mind and body, and therefore mind and brain, are fundamentally different—so different, that it’s surprising to learn of the carefully orchestrated correspondence revealed by the “findings” summarized above. It’s wrong to equate mind with brain (perhaps, to quote Marvin Minsky, “the mind is what the brain does”), but we ought to reject the Cartesian commitments that underlie intuitive dualism—no matter how intuitive they feel.

The second reason is because neuroscientific findings about the mind reveal the broadening scope of science. As our abilities to measure, analyze, and theorize have improved, so has the scope of what we can address scientifically. That’s not new—what is new is the territory that now falls within the scope of science, including the psychology of moral judgment, religious belief, creativity, and emotion. In short: the mind and human experience. We’re finally making progress on topics that once seemed beyond our scientific grasp.

Of course, it doesn’t follow that science can answer all of our questions. There are many, many empirical questions about the mind for which we don’t yet have answers, and some for which we may never have answers. There are also questions that aren’t empirical at all. (Contra Sam Harris, I don’t think science—on its own—will ever tell us how we ought to live, or what we ought to believe.) But the mind and human experience are legitimate topics of scientific study, and they’re areas in which we’re making remarkable, if painstaking, progress. That’s good news to me.

Annenberg University Professor, University of Pennsylvania, with appointments in Wharton, Psychology and Political Science; Author, Expert Political Judgment; (with Dan Gardner) Superforecasting: The Art and Science of Prediction (forthcoming, Sept. 2015)

Thirty-five years ago, I was an insecure assistant professor at the University of California Berkeley—and a curmudgeonly senior colleague from the hard-science side of psychology took me aside to warn me that I was wasting whatever scientific talent I might have. My field, broad-brushed as the soft side of psychology, was well intentioned but premature. Soft-siders wanted to help people but they hadn’t a clue how to do it.

Now I get to play curmudgeon. The recent wave of disclosures about the non-replicability of many soft-side research phenomena suggests that my skeptical elder knew more than I then realized. The big soft-side scientific news is that a disconcertingly large fraction of the news does not hold up to close scrutiny. The exact fraction is hard to gauge but my current guess is at least 25%, perhaps as high as 50%. But historians of science will not have a hard time portraying this epistemic “train wreck” as retrospectively inevitable. Social psychology and overlapping disciplines had evolved into fields that incentivized scholars to get over the talismanic p < .05 significance line to support claims to original discoveries and disincentivized the grunt work of assessing replicability and scoping out boundary conditions. And as the gang of six, Duarte et al, point out in their Behavioral and Brain Sciences article on “ideological diversity will improve social psychology,” the growing political homogeneity of the field selectively incentivized the production of certain types of knowledge: counterintuitive findings that would jar the attentive public into realizing how deeply unconsciously unfair the social order is. This has proven a dangerous combination.

In our rushed quest to establish our scientific capacity to surprise smart outsiders plus help those who had long gotten the short end of the status stick, soft-siders had forgotten the normative formula that Robert Merton formulated in 1942 for successful social science, the CUDOS norms for protecting us from absurdities like Stalinist genetics and Aryan physics. The road to scientific hell is paved with political intentions, sometimes maniacally evil ones and sometimes profoundly well intentioned ones. If you value science as a purely epistemic game, the effects are equally corrosive. When you replace the pursuit of truth with the protection of dogma, you get politically-religiously tainted knowledge. Mertonian science imposes monastic discipline: it bars even flirting with ideologues.

But “prematurity” is a temporal diagnosis. I timed my birth badly but those entering the field today should see the train wreck as a goldmine. My generation’s errors are their opportunities. Silicon-Valley-powered soft-science gives us the means of enforcing Mertonian norms of radical transparency in data collection, sharing and interpretation. We can now run forecasting tournaments around Open Science Collaborations in which clashing schools of thought ante up their predictions on the outcomes of well-designed, large-sample-size studies, earning or losing credibility as a function of rigorously documented track records rather than who can sneak what by which sympathetic editors. Once incentives and norms are reasonably aligned, soft-science should firm up fast. Too bad I cannot bring myself to believe in reincarnation.

This year, researchers at DeepMind, an AI company in London, reported that they taught a computer system how to learn to play 49 simple video games. They didn't teach it "how to play video games," but how to learn to play the games. This is a profound difference. Playing a video game, even one as simple as the 1970s classic game Pong, requires a suite of sophisticated perception, anticipation, and cognitive skills. A dozen years ago, no algorithms could perform these tasks, but today these game-playing codes are embedded in most computer games. When you play a 2015 video game you are usually playing against refined algorithms crafted by genius human coders. But rather than program this new set of algorithms to play a game, the DeepMind AI team programmed their machine to learn how to play the games. The algorithm (a deep neural network) started out with no success in the game and no skill or strategy and then assembled its own code as it played the game, by being rewarded for improving. The technical term is unsupervised learning. By the end of hundreds of rounds, the neural net could play the game as well as human players, sometimes better. In every sense of the word, it learned how to play the games.

This learning should not be equated with "human intelligence." The mechanics of its learning are vastly different from how we learn. It is not going to displace humans or take over the world. However, this kind of synthetic learning will grow in capabilities. The significant news is that learning—real unsupervised learning—can be synthesized. Once learning can be synthesized it can be distributed into all kinds of ordinary devices and functions. It can be used to enable self-driving cars to get better, or for medical diagnosing programs to improve with use.

Learning, like many other attributes we thought only humans owned, turns out to be something we can program machines to do. Learning can be automated. While simple second-order learning (learning how to learn) was once rare and precious, it will now become routine and common. Just like tireless powerful motors, and speedy communications a century ago, learning will quickly become the norm in our built world. All kinds of simple things will learn. Automated synthetic learning won't make your oven as smart as you, but it will make better bread.

Very soon smart things won't be enough. Now that we know how to synthesize learning, we'll expect all things to automatically improve as they are used, just as DeepMind's game learner did. Our surprise in years to come will be in the many unlikely places we'll be able to implant synthetic learning.

We are facing problems from two of our great inventions: money and flushing the toilet.

We all use money, but we can be isolated from money at the same time. Without a doubt, money is one of the greatest human inventions, but it may also be one among the worst ever created.

In the present social, economic system, gold is the standard of money. Although gold is precious, and, as a standard, brings both universality and anonymity to money, it has nothing to do with anything that comes from human beings. While we can do many things with gold standard money in our modern societies, there are no significant connections between the money and ourselves. Thus, it can be hypothesized that whenever we use money, we are isolating ourselves from the world.

Flushing the toilet—a second great invention—also has both positive and negative aspects. While it deals effectively with issues of hygiene, when we flush the toilet we are flushing our excretion into the natural environment and this leads to severe problems.

Here’s an idea which could lead us to a new kind of artistic and scientific world: Can you think of a horizon in which we can both mitigate the problems while keeping the advantages of our current money system? Imagine a scientific method of making odorless powders from our feces, and replacing money with that powder as an alternative to our current system—i.e., “feces standard money (FSM).”

Every morning we can put our powder into reactors located in our village to supply food for the microorganisms that can produce various energies such as methane and biodiesel. We can receive a certain amount of FSM in exchange for the powdered feces, and use the FSM to obtain any equivalent value within a system. Feces, like gold, is limited and precious; nobody can make more than a certain limit, and it can be converted to energy.

Furthermore, everyone can make feces every day. Whenever we produce and use FSM, it will remind us of our being and existence from the bottom line connection between the FSM and the human being. Thus, FSM has meanings from perspectives of economy and minds of the human being.

FSM can automatically become “basic income,” as long as we put feces into the reactors on a daily basis instead of continuing to flush the toilet. We don’t have to argue the feasibility of the concept of basic income with the gold standard of money. We can use both money systems without conflicts.

FSM has similar characters to the local exchange trading system (LETS) but is different in various aspects, from the origin of value (i.e., from us) and the system with it. The FSM is different from other types of credit such as mileage, coupons, and online coins, because it is directly connected to our existence, free will, and intentions as a meaningful act, of not using the flushable toilet.

The dollar has been allocated to a certain quantity of gold. Similarly, FSM can be made out of a certain amount of feces powder. Everyone can earn money from their selection of the ecological toilet, and have meaningful moments as they use the FSM by being connected to both their free will with the selection and their subsequent purchases.

Suggestions: A system with FSM can be designed with an App or other similar ways. FSM can be used along with the present currency to buy something such as gas, coffee, foods, and to pay for other enjoyable activities. For example, take the case of street performers. We can enjoy their work and don't have to pay anything. Instead, the street performer can be offered some FSM with a designed IT system. Payment may not come directly from the audience. Neither the performers nor the audience have to be subject to the present payment system whenever FSM is available. Values depending on the production of energy or other equivalent products from the feces at a designated time or date can be distributed to the participants of the system. Of course, since this system with FSM can't provide everything that we need, conventional money also has to be used.

The proposed money system requires various technologies such as biological processes for energy production—and new industries as well such as “golden electronics” (compared to white electronics) for toilets to ecologically convert feces into powder.

2015 marks the hundredth anniversary of Einstein's announcement of the theory of general relativity. General relativity describes the force of gravity in terms of the curvature of space and time: the presence of matter warps the underlying fabric of the universe, causing light to curve and clocks to slow down in the presence of matter. General relativity supplies us with a physical theory that allows us to describe the cosmos as a whole; it predicts the existence of exotic objects such as black holes; it even supports closed timelike curves that in principle allow travel backward in time. General relativity is a tremendous scientific success story, and its hundredth anniversary has been marked by multiple articles, television shows, scientific conferences, and more to celebrate Einstein's achievement.

Unmentioned in this celebration is the darker story. As soon as Einstein announced his elegant theory, other physicists began trying to reconcile general relativity with quantum mechanics. Quantum mechanics is the physical theory that governs matter at its smallest and most fundamental scales. The last century has also seen tremendous advances in the application of quantum mechanics to study elementary particles, solid state physics, the physics of light, and the fundamental physics of information processing. Pretty much as soon as the print on Einstein's papers had dried, physicists began trying to make a quantum theory of gravity. They failed.

The first theories of quantum gravity failed because scientists did not understand quantum mechanics very well. It was not until a decade after Einstein's results that Erwin Schroedinger and Werner Heisenberg provided a precise mathematical formulation of quantum mechanics. By the beginning of the 1930s, Paul Dirac had formulated a version of quantum mechanics that incorporated Einstein's earlier—and by definition less general—theory of special relativity. Throughout the next half century, in the hands of physicists such as Richard Feynman and Murray Gell-Mann, this special-relativistic version of quantum mechanics, called quantum field theory, provided dramatic advances in our understanding of fundamental physics, culminating by the mid 1970s in the so-called Standard Model of elementary particles. The Standard Model unifies all the known forces of nature apart from gravity: it has been triumphantly confirmed by experiment again and again.

What about a quantum theory of gravity, then? After Dirac, when physicists tried to extend the successful techniques of quantum field theory to general relativity, they failed. This time, they failed because of a knotty technical problem. One of the peculiarities of quantum field theory is that when you try calculate the value of some observable quantity such as the mass of the electron, the naive answer that you obtain is infinity.

Looking more closely, you realize that the interactions between the electron and other particles, such as photons (particles of light), have to be taken into account: these interactions “renormalize” the mass of the electron, making it finite. Renormalization works beautifully in the case of quantum field theory, allowing the prediction of quantities such as the mass of the electron to more than six digits of accuracy. But renormalization fails utterly in the case of quantum gravity: quantum gravity is not renormalizable. Infinity remains infinity. Fail.

More recent decades of failure to quantize general relativity have yielded tantalizing clues. Perhaps the best known result that combines quantum mechanics and gravity is Stephen Hawking's famous proof that black holes are not absolutely black, but in fact emit radiation.

Hawking radiation is not a theory of quantum gravity, however, but of quantum matter moving about on a classical spacetime that obeys Einstein's original non-quantum equations. Loop quantum gravity solves some of the problems of quantum gravity, but exacerbates others: it has a hard time including matter in the theory, for example. Speaking as someone made of matter, I object to theories that do not include it.

One of the primary appeals of String Theory is that it naturally contains a particle that could be identified with the graviton, the quantum of gravity. Sadly, even the most enthusiastic followers of String Theory admit that it is not yet a full self-consistent theory but rather a series of compelling mathematical observations called—with an apparently complete lack of irony—`miracles.'

Long-time practitioners of quantum gravity have advised me that if one wishes to publish in the field, any advance that claims to improve on one aspect of quantum gravity must be offset by making other problems worse, so that net effect is negative. If economics is the dismal science, then quantum gravity is the dismal physics.

The last few years leading up to the centennial of failing to quantize gravity have seen a few glimmers of hope, however. Quantum information is the branch of physics and mathematics that describes how systems represent and process information in a quantum mechanical fashion. Unlike String Theory, quantum information is in fact a theory: it proceeds by orderly conjecture and mathematical proof, with close contact to experiment. Quantum information can be thought of as the universal theory of discrete quantum systems, systems that can be represented by bits or quantum bits.

Recently, researchers in quantum gravity and quantum information have joined forces to show that quantum information theory can provide deep insights into problems such as black hole evaporation, the holographic principle, and the AdS-CFT correspondence. (If these subjects sound esoteric, that's because they are.) Encouragingly, the advances in quantum gravity supplied by quantum information theory do not yet seem to be counterbalanced by back-sliding elsewhere.

We have no idea whether this attempted unification of qubits and gravitons will succeed or fail. Empirical observation of the last century of failure to quantize gravity suggests the latter. With any luck, however, the next hundred years of quantizing gravity will not be so dismal.

Some time early in this century, it seems, the UK may have reached "peak stuff." It is a complex calculation, of course, but it seems that although the world's oldest industrialized economy had grown through most of that period, its consumption of raw materials and fossil fuels had not grown in lockstep as before, but had (save for one markedly cold winter when fuel consumption spiked) consistently declined.

Chris Goodall and a number of other commentators have documented this decoupling extensively: UK government data also shows a reduction in material use from about 12 tons a year per person to around 9 tons from 2000 to 2013. Japan shows a similar pattern.

Some people have contested these findings, of course. (Other people believe they are true, but wish that they weren't widely known.) But there is enough evidence worldwide to show that patterns of consumption and status-seeking do change, and that intangible goods are replacing physical ones in many domains. Not only are there the obvious, comparatively trivial examples where, say, music and film downloads have replaced CDs and DVDs, but car-mileage seems to have peaked, as have car purchases. Astoundingly to anyone who has seen American Graffiti, half of US eighteen-year-olds do not have a driver's license.

But there seem to be multiple forces at work which are all aligned towards a lower emphasis on material consumption. One of them may be simple satiety—it is difficult to see the benefits of not owning a car until one has owned one for a few years; it is only by travelling long-haul a few times that one may discover that your favorite place to spend your free time is a lake sixty miles from home. Now that jet travel is affordable to most people, it is perfectly acceptable (in fact rather an ornament) in wealthy British circles to take your main holiday in Britain.

Hipsterisation of various categories (beer, gin, coffee, etc) is also evidence of a complementary trend—where people seek value and status in increasingly hair-splitting distinctions between basic goods, rather than spending discretionary income on greater quantities of such goods, on or non-essential purchases.

The evolutionary psychologist Geoffrey Miller has even ingeniously attributed this change in behavior to the creation of online social media, which change the whole nature of status signaling—where sharing experiences may have gained signaling power at the expense of possessions.

More and more economic value is becoming entirely divorced from the physical attributes of a thing, and resides in intangibles. London's most expensive street consists of terraces of houses which most wealthy Victorians would have found laughably small; it is the fashionable address which gives them their value, and the fact that living in the center of a city is now deemed more fashionable than living in suburbia.

The great thing about intangible value, I suppose, is that its creation involves very little environmental damage. It may help disabuse people of the belief that the only way to save the planet is for us to impoverish ourselves. What it may mean is that those same human qualities of status-rivalry and novelty-seeking which can be so destructive might be redirected even if they cannot be eliminated.

The "abc conjecture," first proposed in 1985, asserts a surprising connection between the addition and multiplication of whole numbers. (The name comes from that amiable equation, a + b = c.) It appears to be one of the deepest and most far-reaching unresolved conjectures in mathematics, intimately tied up with Roth's theorem, the Mordell conjecture, and the generalized Szpiro conjecture.

Three years ago, Shinichi Mochizuki of the University of Kyoto claimed to have proved the abc conjecture—potentially a stunning advance in higher mathematics. But is the proof sound? No one has a clue.

Near the end of last year, some of the world's leading experts on number theory convened in Oxford to sort abc out. They failed.

Mochizuki's would-be proof of the abc conjecture uses a formalism he calls "inter-universal Teichmüller theory" (IUT), which features highly symmetric algebraic structures dubbed "Frobenioids." At first the problem was that no one (except, we must suppose, its creator) could understand this new and transcendently abstract formalism. Nor could anyone see how it might bear on abc.

By the time of the Oxford gathering, however, three mathematicians—two of them colleagues of Mochizuki's at Kyoto, the third from Purdue in the U.S.—had come to see the light. But when they in turn tried to explain IUT and Frobenioids, their peers had no idea what they were talking about—"indigestible," one of the participants called their lectures.

In principle, checking a proof in mathematics shouldn't require any intelligence or insight. It's something a machine could do. In practice, though, a mathematician never writes out the sort of austerely detailed "formal" proof that a computer might check. Life is too short. Instead, she (lady-friend of mine) offers a more or less elaborate argument that such a formal proof exists—an argument that, she hopes, will persuade her peers.

With IUT and abc, this business of persuasion has got off to a shaky start. So far, converts to the church of Mochizuki seem incapable of sharing their newfound enlightenment with the uninitiated. The abc conjecture remains a conjecture, not a theorem. That might change this summer, when number theorists plan to reconvene, this time in Kyoto, to struggle anew with the alleged proof.

So what's the news? It's that mathematics—which, in my cynical moods, I tend to regard as little more than a giant tautology, one that would be as boring to a trans-human intelligence as tic-tac-toe is to us—is really something weirder, messier, more fallible, and far more noble.

And that "Frobenioids" is available as a name for a Brooklyn indie band.

You may not like it! But artificial intelligence jumped a bit closer this year with the development of “Bayesian Program Learning,” by Lake, Salakhutdinov, and Tenenbaum, published in Science. It’s news because for decades I’ve been hearing about how hard it is to achieve artificial intelligence, and the most successful methods have used serious brute force. Methods based on understanding the symbols and logic of things and language have had a tough time. The challenge is to invent a computer representation of complex information, and then enable a machine to learn that information from examples and evidence.

Lake et al. give a mathematical framework, an algorithm, and a computer code that implements it, and their software has learned to read 1623 handwritten characters in 50 languages as well as a human being. They say “Concepts are represented as simple probabilistic programs—that is, probabilistic generative models expressed as structured procedures in an abstract description language.” Also, a concept can be built up by re-using parts of other concepts or programs. The probabilistic approach handles the imprecision of both definitions and examples. (Bayes’ theorem tells us how to compute the probability of something complicated, if we know the probabilities of various smaller things that go into the complicated thing.) Their system can learn very quickly, sometimes in one shot, or from a few examples, in a human-like way, and with human-like accuracy. This ability is in dramatic contrast to competing methods depending on immense data sets and simulated neural networks, which are always in the news.

So now there are many new questions: How general is this approach? How much structure do humans have to give it, to get it started? Could it really be superior in the end? Is this how living intelligent systems work? How could we tell? Can this computer system grow enough to represent complex concepts that are important to humans in daily life? Where are the first practical applications?

This is a long-term project, without any obvious limits to how far it can go. Could this method be so efficient that it doesn’t take a super-duper supercomputer to achieve or at least represent artificial intelligence? Insects do very well with a tiny brain after all. More generally, when do we get really accurate transcriptions of multi-person conversations, instantaneous machine language translation, scene recognition, face recognition, self-driving cars, self-directed UAVs safely delivering packages, machine understanding of physics and engineering, machine representation of biological concepts, and machine ability to read the Library of Congress and discuss it in a philosophy or history class? When will my digital assistant really understand what I want to do, or tell me what I ought to do? Is this how the Intelligent Mars Rover will hunt for signs of life on Mars? How about military offense and defense? How could this system implement Asimov’s three laws of robotics, to protect humans from robots? How would you know if you should trust your robot? When will people be obsolete?

I’m sure many people are already working on all of these questions. I see many opportunities for mischief, but the defense against the dark arts will push very rapid progress too. I am both thrilled and frightened.

Climate change is an enormous challenge. Rapid decarbonization of manufacturing, electricity generation and transportation is critical and may become a crisis because of non-linear effects. 2015 brought not-widely-disseminated news of the commercial availability of three substantial scientific breakthroughs that can significantly accelerate decarbonization.

1) Decarbonizing Concrete; Commoditizing CO2

After water, concrete is the most widely used material in the world. The manufacture of Portland cement for use in concrete accounts for up to 5 percent of global anthropogenic emissions. A new “Solidia cement,” invented by Dr. Richard Riman of Rutgers University, can be made from the same ingredients as Portland cement and in the same kilns but at lower temperature while incorporating less limestone thus emitting substantially less CO2 in its manufacture. Unlike Portland cement, which consumes water to cure, this new cement cures by consuming CO2. Concrete products made from this new cement have their CO2 footprint reduced by up to 70 percent. Thousands of tons of the new cement have been manufactured and 2015 brings news that large manufacturers are now modifying their factories to use it—rather than Portland cement—to make concrete; its widespread adoption would multiply the demand for industrial CO2 substantially, creating a strong economic incentive for CO2 capture and reuse.

Previous attempts at introducing radically low-carbon cements have all failed to scale—because they required raw materials that were not ubiquitous, necessitated new and expensive capital equipment, and/or because of the large range of material properties required by regulation or for specific applications. Despite overcoming these problems and having lower cost and better performance, rapid adoption in an existing infrastructure requires adoption to be incredibly simple—in this case by changing only a single step of the manufacturing process to cure with CO2 rather than water.

Can we similarly expect to reduce the CO2 footprint of other high-embodied-energy materials such as steel and aluminum while reusing the existing infrastructure? A decade-long search found no suitable candidate breakthroughs, so these decarbonizations may unfortunately require a much slower process of redesigning products to use lower-embodied-energy materials like structural polymers and fibers.

2) Scalable Wind Turbines for Distributed Wind

More than a billion people, mostly in rural areas in the developing world, lack access to a reliable grid and electricity—it matters greatly whether they will get electricity from renewables or fossil fuels. Wind turbines today are the cheapest renewable but only in very large multi-megawatt Utility-Scale units unsuitable for distributed generation (DG); at smaller sizes the performance of existing wind turbine designs degrades substantially. A new type of shrouded wind turbine, invented by Dr. Walt Presz and Dr. Michael Werle of Ogin Energy, saw its first multi-unit deployment at Mid-Scale (100kW-rated range) in 2015. This new turbine’s shroud system pumps air around the turbine so that it is efficient at both Mid- and Small-Scale sizes and at lower wind speeds, thus supporting DG and microgrids.

A recent analysis shows Utility-Scale Wind the cheapest renewable with unsubsidized cost at about $80/MWh, Solar PV at about $150/MWh; conventional Mid-Scale Wind is $240/MWh—too expensive to make a substantial contribution. The new shrouded turbines provide electricity at half the cost of the conventional Mid-Scale turbines today, and will be cost-competitive with Utility-Scale Wind when they are in volume production.

We need to deploy enormous amounts of renewables to fully decarbonize electricity generation and enable the necessary decommissioning of most of the existing fossil-fuel consuming generating equipment. Wind can be deployed extensively much more quickly, safely, and cheaply than the often proposed scale-up of nuclear energy, and can be combined with grid storage such as batteries to make it fully dispatchable. If we get serious about decarbonization, Small- and Mid-Scale turbines can be quickly scaled to high-volume production using existing manufacturing infrastructure, much as was done for materiel during WW II.

Having cost-effective wind at all scales complements Solar PV and, with grid storage, completes a portfolio that can further accelerate the already dramatic trend towards most new electric capacity being supplied by renewables.

3) Room Temperature Ionic Electrolyte for Solid State Batteries

Current lithium-ion batteries use a flammable liquid electrolyte and typically incorporate materials that further increase the fire danger. Most contain expensive metals such as lithium, cobalt, and nickel. 2015 brings publication of the existence of a new polymer electrolyte, invented by Michael Zimmerman of Ionic Materials, that is the first solid to have commercially practical ionic conductivity at room temperature. The polymer is also inherently safe, self-extinguishing when set on fire. It creates a substantially different chemical environment than a liquid, supporting novel and abundant cathode materials such as sulfur, which is high capacity, light, and inexpensive, and novel and inexpensive metal anodes, thereby supporting multivalent species such as Zn2+. Many desirable battery chemistries, infeasible with liquid electrolytes, are newly possible.

This scientific breakthrough, shown only in the 2030’s on most battery-industry roadmaps, has long been desired because solid batteries can be substantially cheaper, safe, and store more energy. Solid polymer batteries can be manufactured using mature and inexpensive scale-manufacturing equipment from the plastics industry.

15 percent of global CO2 fossil fuel emissions result from wheeled transportation. India and China’s fleets will grow substantially in the years ahead; whether energy for these additional vehicles is provided by renewables or fossil fuels will make a significant difference in global emissions. Low cost, safe and high-capacity batteries can greatly accelerate the electrification of transportation and these fleets beyond the current modest projections.

In the 21st century we need to stop combusting fossil fuels. Electrochemistry—both better batteries and fuel cells—has far greater potential than is generally realized and can displace most combustion.

There are other gas- and liquid-based technologies that we can hope to convert to solid-state to reduce their CO2 impact, such as cooling which generally uses a liquid-gas phase transition today. I hope that the future brings news of a solid-state cooling breakthrough that, like the above technologies, can be quickly taken to scale.

It's not every year that Edge echoes South Park. I guess everyone's trying to figure out what's real right now. The 2015 South Park season revolved around people losing the ability to distinguish between news and advertising. One day, they wake up broke, at war, and unable to easily distinguish friend from foe. News, as a concept, is gone. Science, as a concept, is gone. In information warfare, the assumption that reliable, low-context communication is even possible recedes into fantasy, taking with it both news and science and replacing them with politics and marketing. I think that the real news, viewed from behind the new, extra-strength Veil of Maya, is what you think you see with your own eyes AND have checked out against the analytic parts of the scholarly literature. Here's what I've got.

Last November, I was visiting Francisco Marroquin University in Guatemala (UFM), which is known primarily for being the most Libertarian University in the world. While there, on the floor of the economics department, my co-conspirator found local currency worth almost exactly twenty US dollars. Technically, the money wasn't visible from the sidewalk, but the signs announcing gasoline prices clearly are. In the last few years, I have observed those prices decoupling, both from the price of oil and from one another, whether across town or even across the street. Growing up, I always noticed whether the gas prices on opposite sides of the road differed by one, two, or sometimes even three cents. Today, such prices typically differ by more like twenty cents. I recently saw two stations across the street from one another with prices differing by $.36/gallon and two stations a mile apart charging respectively $2.49/gallon and $3.86/gallon. For a median American driver, $.20/gallon, invested at historically normal rates of return, would add up to about $1500 over the next decade. Median retirement savings for families aged 55-64 is only $15,000, and for families with retirement accounts, median savings are still only $150,000.

I'm OK with people not behaving like Homo Economicus, but if broke people are becoming less economically rational with time, this suggests that people don't feel that they can predict the future in basic respects. That they aren't relying on savings to provide for their basic needs. Who can blame them for financial recklessness? Theory, as well as practice, tells us that their leaders have been setting an ever worse example for generations.

Financial economics provides the analytic literature on economic caution and risk. In 1987, Larry Summers & Brad DeLong showed that given a risk premium, a standard assumption in financial economics, irrational noise traders crowd out rational actors over time. When Peter Thiel talks about the shift from "concrete optimism" to "abstract optimism”; I think that he is characterizing the pattern that is selected for by this dynamic. This shift towards noise trading inflates equity prices, concentrates wealth, and causes more speculative assets to command higher prices each decade than similarly speculative assets would have commanded in the previous decade. With 84 percent of corporate valuations now taking the form of intangibles, up from 16 percent forty years ago—that sounds like the world I see around me. The overall divergence of map from territory in economic settings ultimately means the annihilation of strategy as we know it for most people, making apparent economic prudence a predictably loosing strategy, which means that in the long run, if we can’t figure out a better way to aggregate local economic information, we won’t have the patience to effectively use that information.

The most long-range portentous event of 2015 was NASA’s New Horizons spacecraft arrowing by Pluto, snapping clean views of the planet and its waltzing moon system. It carries an ounce of Clyde Tombaugh's ashes, commemorating his discovery of Pluto in 1930. Tombaugh would have loved seeing the colorful contrasts of this remarkable globe, far out into the dark of near-interstellar space. Pluto is now a sharply-seen world, with much to teach us.

As the spacecraft zooms near an iceteroid on New Year’s Day, 2019, it will show us the first member of the chilly realm beyond, where primordial objects quite different from the wildly eccentric Pluto also dwell. These will show us what sort of matter made up the early disk that clumped into planets like ours—a sort of family tree of worlds. But that’s just an appetizer.

New Horizons is important not just for completing our first-look at every major world in the solar system. It points outward, to a great theatre in the sky, where the worlds of the galaxy itself are on display.

Beyond Pluto looms a zone where the Sun’s mass acts as a giant lens, its gravitation focusing the light of other stars to a small area. Think of it as gravity gathering starlight into an intense pencil, focused down as dots on a chilly sphere. Einstein calculated such gravitational bending of light in 1912, though Newton knew the effect should occur in his own theory of mechanics and optics.

Images of whole galaxies made by this effect were not discovered until 1988. Such magnification of light from a star and the planets near it naturally creates a telescope of unparalleled power. It can amplify images by factors that can vary from a hundred million to a quadrillion, depending on frequency.

This suggests using such power to study worlds far across the interstellar reaches. We have already detected over two thousand planets around other stars, thanks to the Kepler mission and other telescopes. We can sense the atmospheres of some, when they pass across our view of their stars, silhouetted against that glare. Many more will come.

Our space telescopes envisioned for the next several decades can only tease out information about a planet at interstellar distances by studying how light reflects or absorbs changes. At best, such worlds will be dots of faint light.

But at the lensing distance, under enormously better resolution, we can see the worlds themselves—their atmospheres and moons, their seas and lands, perhaps even their cities.

Hearty New Horizons now zooms along at about 15 kilometers a second, or more usefully said, at about three Astronomical Units (AU, the distance between Earth and our sun) in a year, relative to the sun. The focus spot of the sun is 550 AU out, as Einstein predicted in 1936. New Horizons will take 180 years to get to that focus and be long dead as its nuclear power supply fades. So future missions to put a telescope out there demands speeds ten or more times faster. (Voyager, flying after thirty-eight plus years, is only 108 AU away from earth.)

We know of ways to propel spacecraft to such speeds. Most involve flying near the sun and picking up velocity by firing rockets near it, or getting a boost from its intense light using unfurled solar sails, and other astro-tricks. Those feats we can fashion within decades, if we wish.

Our goal could be to put an observing spacecraft that can maneuver out at the focus of “God’s zoom lens”—a 70 billion mile long telescope that light takes over three days to traverse. An observing spacecraft could see whatever is behind the sun from it, many light years away.

This would vastly improve our survey of other worlds, to pick off strings of stars and examine their planets. Using the sun as a lens works on all wavelengths, so we could look for signs of life—say, oxygen in an atmosphere—and perhaps even eavesdrop on aliens’ radio stations, squawking into the galactic night.

A first, such a telescope could scrutinize Alpha Centauri’s planets, if it has some, the next big step before trying to travel there. The craft could trace out a spiral pattern perpendicular to its outward path, slightly shifting its position relative to scan the Alpha Centauri system. Then look further still—because the focus effect remains beyond 550 AU, as a spacecraft moves outward, still seeing the immense magnifications.

New Horizons maybe the best-named spacecraft of all, for it does indeed portend fresh, bold perspectives.

Surprising images of dwarf planet Pluto from a flyby of the New Horizons spacecraft put space exploration back in the headlines as one of the biggest science stories of 2015. Instead of a barren, frigid globe, Plato proved to be colorful, contrasty, and complex, with diverse geological structure including mountains, valleys, and plains of water and nitrogen ice, evidence of past and present glacial flows, possible volcanoes spewing water ice from a warmer core, and a thin atmosphere that extends hundreds of miles above the planetary surface. Similar insights are being gleaned from Chiron and the smaller of Pluto's five moons.

2015 also brought Pluto news of a more arcane sort. The historic 24-inch Clark refractor telescope of Lowell Observatory was refurbished and opened to the public for viewing. Why mention Lowell and Earth-based optical astronomy? As a young Lowell employee, Clyde Tombaugh discovered Pluto in 1930 on photographic plates taken with a Lowell instrument. For many, telescopes, whether optical leviathans or the modest backyard variety, are spaceships for the eye and mind that provide a compelling sensory immediacy lacking in the pricier technological tour de force of a spacecraft. Recall the aesthetic impact of the starry night viewed from a dark country path or seeing Saturn for the first time through a telescope. Although modern Earth-based telescopes continue to provide astronomical breakthroughs, old telescopes and the observatories that house them survive as domed, verdigris-covered cathedrals of science. The Pluto flyby of 2015 is an occasion to celebrate space exploration new and old, and the value of looking upward and outward.

A year and half ago, the scientific community and the press trumpeted the claim by a team of scientists that they had found definitive proof that the universe began with a Big Bang followed by a period of accelerated expansion, known as inflation. Their proof was that the light produced in the infant universe and collected by their detectors exhibited a distinctive pattern of polarization that could only be explained if the large scale structure of the universe was set when the temperature and density of the universe were extraordinarily high, just as posited in the Big Bang inflationary picture.

Over the ensuing year, though, it became clear that the claim was a blunder: in searching for a cosmic signal from the distant universe, the team had not taken proper account of the polarization of light that occurred nearby when it passed through the dust in our Milky Way on the way to their detectors. The new claim from the team, published in recent months, is that there is no sign of the cosmic polarization they had been seeking despite an extensive search with extraordinarily sensitive detectors.

The retraction received considerable attention but the full import of the news has not been appreciated: we now know thatthe Big Bang cannot be what we thought it was.

The prevailing view has been that the Big Bang was a violent high-energy event during which space, time, matter and energy were suddenly created from nothing in a distorted, non-uniform distribution. To account for the undistorted nearly uniform universe we actually observe, many cosmologists hypothesize a period of rapid stretching (inflation) just after the bang when the concentration of energy and matter was still very high. If there were inflationary stretching only, the universe would become perfectly smooth, but there is always quantum physics in addition to stretching and quantum physics resists perfect smoothness.

At the high concentrations of energy required for inflation, random quantum fluctuations keep generating bumps and wiggles in the shape of space and the distribution of matter and energy that should remain when inflation ends. The quantum-generated irregularities should appear today as hot spots and cold spots in the pattern of light emanating from the early universe, the so-called cosmic background radiation. Indeed, the hot and cold spots have been observed and mapped in numerous experiments since the COBE satellite detected the first spatial variations in the cosmic background radiation temperature in 1992.

The problem is that, when the concentration of energy is high, the quantum-generated distortions in space should modify the way light scatters from matter in the early universe and imprint a spiraling pattern of polarization across the cosmos. It was the detection of this spiraling pattern (referred to as B-mode) that was claimed as proof of the Big Bang inflationary picture, and then retracted. The failure to detect the B-mode pattern means that there is something very wrong with the picture of a violent Big Bang followed by a period of high energy-driven inflation. Whatever processes set the large-scale structure of the universe had a to be a gentler, lower-energy process than has been supposed.

Simply lowering the energy concentration at which inflation starts, as some theorists have suggested, only leads to more trouble. This leaves more time after the Big Bang for the non-uniform distribution of matter and energy to drive the universe away from inflation. Starting inflation after the Big Bang and having enough inflation to smooth the universe becomes exponentially less likely as the energy concentration is lowered. The universe is more likely to emerge as too rough, too curved, too inhomogeneous compared to what we observe.

Something more radical is called for. Perhaps an improved understanding of quantum gravity will enable us to understand how the Big Bang and inflation can be discarded in favor of gentler beginning. Or perhaps the Big Bang was actually a gentle bounce from a previous period of contraction to the current period of expansion. During a period of slow contraction, it is possible to smooth the distribution of space, matter and energy and to create hot spots and cold spots without creating any B-modes at all.

As the news sinks in, scientists will need to rethink depending on whether forthcoming, more sensitive efforts to detect a B-mode pattern find anything at all. Whatever is found, our view of the Big Bang will be changed, and that is newsworthy.

The end of the year saw two stories about pathogens, one hopeful and the other less so. The hopeful one is the advancing ability to insert genes that are desirable (from humans’ point of view) into organisms and facilitating that gene’s spread through the population.

Consider the case of malaria, a focus of current efforts. Inserting a gene that inhibits the spread of malaria into a mosquito genome is helpful, but to only a limited degree: if the gene doesn’t spread in wild populations, then its effects will be fleeting. If, however, mosquitoes are released that have the gene in question as well as genes that facilitate the spread of the gene, then the effects can be long-lasting.

The less hopeful case is so-called “super-gonorrhea,” strains of the pathogen that are resistant to the antibiotics currently in use. As is well known, the use of antibiotics gives an advantage to strains of pathogens that are resistant. The tools we use to treat disease become the means by which we make the diseases harder to treat. Cases of super-gonorrhea have appeared in England, leading to a certain amount of concern.

Malaria still infects tens of millions of people and causes hundreds of thousands of deaths. Of course global climate change and political strife are important issues, but pathogens still account for millions of lives lost around the world.

Historically, as illustrated by the super-gonorrhea case, fighting pathogens has been something of an arms race: humans build better weapons, pathogens evolve counter-strategies. The present age is seeing a confluence of a number of advances that hold the promise of turning the arms race into something that might be more like a winnable war. First is progress in genetics, including techniques for inserting genes into genomes. Second is a sophisticated understanding of evolution and a fluorescence of ideas about how to harness it: in the past, evolution worked against us, as in the case of resistant strains of pathogens. We are becoming better at harnessing it. And third is a growing sophistication in thinking about systems. It is unlikely that the same sorts of mistakes will be made in the past, in which simplistic ecological interventions led to somewhat disastrous outcomes, as in the case of cane toads in Australia. Our view of ecosystems is more humble and more sophisticated than ever before.

It seems likely that pathogens, of one sort or another, are likely to be important for a substantial period of time: this is because they are moving targets with tremendous capacity for harm. On the other hand, recent advances hold the promise of taming them in a way that we have not previously seen.

We’ve been hearing the tales of doom for quite a few years now: the breathtaking promiscuity of bacteria, which allows them to mix and match their DNA with others’ to an extent that puts Genghis Khan to shame, has increasingly allowed them to accumulate genetic resistance to more and more of our antibiotics. It’s been trumpeted for decades that the rate at which this occurs can be slowed by careful use, especially by not ceasing a course of antibiotics early—but inevitably there is a lack of compliance, and here we are with MRSA, rife in hospitals worldwide, and other major species becoming more broadly antibiotic-resistant with every passing year. The bulk of high-profile expert commentary on this topic is becoming direr with ever passing year.

But this pessimism rests entirely on one assumption: that we have no realistic prospect of developing new classes of antibiotics any time soon, antibiotics that our major threats have not yet seen and thus not acquired resistance to. And it now seems that that assumption is unwarranted. It is based on history—on the fact that no new antibiotic class with broad efficacy has been identified for decades. But very recently, a novel method was identified for isolating exactly those—and it seems to work really, really well.

It arose from a case of sheer chutzpah. Scientists from Boston and Germany got together and reasoned as follows:

Antibiotics are generally synthesised in nature by bacteria (or other microbes) as defences against each other.

We have identified antibiotics in the lab, and thus necessarily only those made by bacterial species that we can grow in the lab.

Almost all bacterial species cannot be grown in the lab using practical methods.

That hasn’t changed for decades.

But those bacteria grow fine in the environment, typically the soil.

So… can we isolate antibiotics from the soil?

And that’s exactly what they did. They built a device that allowed them to isolate and grow bacteria in the soil itself, with molecules freely moving into and out of the device, thereby sidestepping our ignorance of which such molecules actually matter. And then they were able to isolate the compounds that those bacteria were secreting and test them for antibiotic potency. And it worked. They found a completely new antibiotic that has already been shown to have very broad efficacy against several bacterial strains that are resistant to most existing antibiotics.

And as if that were not enough, here’s the kicker. This was not some kind of massive high-throughput screen of the kind we so often hear about in biomedical research these days. The researchers tried this approach just once, in essentially their back yard, on a very small scale, and it STILL worked the first time. What that tells us is that it can work again—and again, and again.

Don’t get me wrong—there is certainly no case for complacency at this stage. This new compound and those discovered by similar means will still need to grind their way through the usual process of clinical evaluation—though, it must be said, there is reason for considerable optimism that that process is dramatically speeding up, with the recent case of an Ebola vaccine being a case in point. But still, even though any optimism must for now be cautious, it is justified. Pandemics may not be our future after all.

On July 4, 2012, the European Organization for Nuclear Research (CERN) announced the discovery of the Higgs boson. While this was big news in fundamental physics, it was not surprising. The existence of the Higgs boson, or something like it, was necessary for the consistency of the Standard Model of particle physics, established in the 1970s and now supported by an extraordinary amount of experimental data. Finding the Higgs was central to confirming the Standard Model. However, despite the well-deserved attention accorded to the discovery of the Higgs, this was not the biggest news. The biggest recent news in fundamental physics is what has NOT been discovered: superparticles.

The theory of supersymmetry—that all existing particles are matched by “superpartners” having opposite spins—was introduced in the 1970s, soon after the Standard Model was established, and quickly became a darling of theoretical physicists. The theory helped solve a fine tuning problem in the Standard Model, with superparticles balancing the quantum contributions to existing particle masses and making the observed masses more natural. (Although, why particles have precisely the masses they do remains the largest open question in fundamental physics.) Also, for proponents of Grand Unified Theories (GUTs), the three strengths of the known Standard Model forces more perfectly converge to one value at high energies if superparticles exist. (Although a similar convergence can be achieved more simply by adding a handful of non-super bosons.) And, finally, supersymmetry (SUSY) became a cornerstone of, and necessary to, superstring theory—the dominant speculative theory of particle physics.

One of the strongest motivations for constructing the Large Hadron Collider (LHC), along with finding the Higgs, was to find superparticles. In order for SUSY to help the Standard Model’s naturalness problem, superparticles should exist at energies reachable by the LHC. During the collider’s first run, the anticipation of superparticles at CERN was palpable—it felt as if a ballroom had been set up, complete with a banner: “Welcome home SUSY!” But superparticles have not shown up to the party. The fact that the expected superparticles have not been seen puts many theorists, including string theorists, in a scientifically uncomfortable position.

Imagine that you had a very vivid dream last night that you saw a unicorn, probably in your backyard. The dream was so vivid that the next day you go into your backyard and look around, expecting to find your unicorn. But it’s not there. That is the position string theorists and other SUSY proponents now find themselves in. You may claim, correctly, that even though you now know there is no unicorn in the backyard, the Bayesian expectation that the unicorn is actually hiding in the closet has increased! You are “narrowing in” on finding your unicorn! This is precisely the argument SUSY proponents are presenting now that the LHC has failed to find superparticles near the electroweak energy scale. Yes, it is correct that the probability the unicorn is in the closet, and that superparticles might be found during the current LHC run, has gone up. But do you know what has gone up more? The probability that the unicorn and superparticles do not exist at all. A unicorn would be a wonderful and magical animal, but maybe it, and SUSY, and superstrings, really just don’t exist, and it’s time to think about other animals.

The most exciting recent scientific news is about science itself: how it is funded, how scientists communicate with another, how findings get distributed to the public—and how it can go wrong. My own field of psychology has been Patient Zero here, with well-publicized cases of fraud, failures to replicate important studies, and a host of concerns, some of them well-founded, about how we do our experiments and analyze our results.

There’s a lot to complain about with regard to how this story has played out in the popular press and over social media. Psychology—and particularly social psychology—has been unfairly singled out. The situation is at least as bad in other fields, such as cancer research. More importantly, legitimate concerns have been exaggerated and used by partisans on both the left and the right to dismiss any findings that don’t fit their interests and ideologies.

But it’s a significant story, and a lot of good can come from it. It’s important for non-scientists to have some degree of scientific literacy, and this means more than a familiarity with certain theories and discoveries. It requires an appreciation of how science works, and how it stands apart from other human activities, most notably religion. A serious public discussion of what scientists are doing wrong and how they can do better will not only lead to better science, it will help advance scientific understanding more generally.

If we knew that AK-47-wielding terrorists were destined to kill 1,000 people in the U.S. in 2016—a thinkable possibility—then we should be afraid . . . albeit 1/10th as afraid of other homicidal gun violence (which kills more than 10,000), and 1/30th as fearful of riding in a motor vehicle, where more than 30,000 Americans die each year. Yet several recent surveys indicate we are much less fearful of the greater, everyday threats than of the dreaded horror.

The hijacking of our rationality by fears of terrorist guns highlights an important and enduring piece of scientific news: we often fear the wrong things.

Shortly after 9/11, when America was besieged by fear, I offered a calculation: If we now flew 20 percent less and instead drove half those unflown miles, then—given the greater safety of scheduled airline flights—we could expect about 800 more people to die on our roads. A German colleague, Gerd Gigerenzer, later checked that prediction. He reported that, actually, in the year after 9/11 “an estimated 1,500 Americans died on the road in the attempt to avoid the fate of the [246] passengers who were killed in the four fatal flights.” Long after 9/11, the terrorists were still killing us.

Why? Why do we fear flying, when, for most of us, the most dangerous part of our trip is the drive to the airport? Why do terrorist fears so effectively inflate our stereotypes of Muslims, inflame us/them thinking, and make many of us Christians forget the ethics of Jesus (“I was a stranger and you welcomed me”)?

Underlying our exaggerated fears is the “availability heuristic”: We fear what’s readily available in memory. Vivid, cognitively available images—a horrific air crash, a mass slaughter—distort our judgments of risk. Thus, we remember—and fear—disasters (tornadoes, air crashes, attacks) that kill people dramatically, in bunches, while fearing too little the threats that claim lives one by one. We hardly notice the half-million children quietly dying each year from rotavirus, Bill Gates once observed—the equivalent of four 747s full of children every day. And we discount the future (and its future weapon of mass destruction, climate change).

If only such deaths were more dramatic and immediate. Imagine (to adapt one mathematician’s suggestion) that cigarettes were harmless—except, once in every 25,000 packs, for a single cigarette filled with dynamite. Not such a bad risk of having your head blown off. But with 250 million packs a day consumed worldwide, we could expect more than 10,000 gruesome daily deaths (the approximate actual toll of cigarette smoking)—surely enough to have cigarettes banned.

News-fed, cognitively available images can make us excessively fearful of infinitesimal risks. And so we spend an estimated $500 million per U.S. terrorist death but only $10,000 per cancer death. As one risk expert explained, “If it’s in the news, don’t worry about it. The very definition of news is ‘something that hardly ever happens.’”

It’s entirely normal to fear violence from those who despise us. But it’s also smart to be mindful of the realities of how most people die, lest the terrorists successfully manipulate our politics. With death on their minds, people exhibit “terror management.” They respond to death reminders by derogating those who challenge their worldviews. Before the 2004 election, reported one research team, reminders of 9/11 shifted people’s sympathies toward conservative politicians and antiterrorism policies.

Media researcher George Gerbner’s cautionary words to a 1981 congressional subcommittee ring true today: “Fearful people are more dependent, more easily manipulated and controlled, more susceptible to deceptively simple, strong, tough measures and hard-line postures.”

Senior Research Fellow, Centre for Research in the Arts, Social Sciences and Humanities, University of Cambridge; Director, Wolfson College Press Fellowship Programme; Columnist, the Observer; Author, From Gutenberg to Zuckerberg

The most interesting news came late in 2015—on November 10, to be precise, when László Babai of the University of Chicago announced that he had come up with a new algorithm for solving the graph-isomorphism problem. This algorithm appears to be much more efficient than the previous “best” algorithm, which has ruled for over thirty years. Since graph isomorphism is one of the great unsolved problems in computer science, if Babai’s claim stands up to the kind of intensive peer-review to which it will now be subjected then the implications are fascinating, not least because we may need to rethink our assumptions about what computers can and cannot do.

The graph isomorphism problem seems deceptively simple: how to tell when two different graphs (which is what mathematicians call networks) are really the same in the sense that there’s an “isomorphism”—a one-to-one correspondence between their nodes that preserves each node’s connections—between them. Easy to state, but difficult to solve, since even small graphs can be made to look very different just by moving their nodes around. The standard way to check for isomorphism is to consider all possible ways to match up the nodes in one network with those in the other. That’s tedious but feasible for very small graphs, but it rapidly gets out of hand as the number of nodes increases. To compare two graphs with just ten nodes, for example, you’d have to check over 3.6 million (i.e. 10 factorial) possible matchings. For graphs with 100 nodes you’re probably looking at a number bigger than all the molecules in the universe. And in a Facebook age, networks with millions of nodes are commonplace.

From the point of view of practical computing, factorials are really bad news because the running time of a factorial algorithm can quickly escalate into billions of years. So the only practical algorithms are those for problems whose solutions can be expressed as polynomials (e.g. n-squared or n-cubed, where n is the number of nodes) because running times for them increase much more slowly than those for factorial or exponential functions.

The intriguing—tantalizing—thing about Babai’s algorithm is that it is neither pure factorial nor polynomial but what he calls “quasi-polynomial.” It’s not clear yet what this means, but the fuss in the mathematics and computer science community suggests that while the new algorithm might not be the Holy Grail, it is nevertheless significantly more efficient than what’s gone before.

If that turns out to be the case, what are the implications? Well, firstly, there may be some small but discrete benefits. Babai’s breakthrough could conceivably help with other kinds of computationally difficult problems. For example, in genomics researchers have been trying for years to find an efficient algorithm for comparing the long strings of chemical letters within DNA molecules. This is a problem analogous to that of graph isomorphism and any advance in that area may have benefits for genetic research.

But the most important implication of Babai’s work may be inspirational—in reawakening mathematicians’ interests in other kinds of hard problems that currently lie beyond the reach of even the most formidable computational resources. The classic example is the public-key encryption system on which the security of all online transactions depends. This works on asymmetry: it is relatively easy to take two huge prime numbers and multiply them together to produce an even larger number. But—provided the original primes are large enough—it is computationally difficult (in the sense that it would take an impracticable length of time) to factorize the product, i.e. determine the two original numbers from which it was calculated. If, however, an efficient factorizing algorithm were to be found, then our collective security would evaporate and we would need to go back to the drawing board.

Amid all the confusing fluctuations in dietary fashion to which Americans have been exposed since the 1960s, one recommendation has remained unchallenged. Beginning in the 1960s and until 2015 the Americans have been getting consistent dietary advice: fat, especially saturated fat, is bad for your health. By the 1980s, the belief equating a low-fat diet with better health had become enshrined in the national dietary advice from the US Department of Agriculture and was endorsed by the surgeon general. Meanwhile, as Americans ate less fat, they steadily became more obese.

Of course, the obesity epidemic probably has many causes, not all well understood. But it is becoming clear that the misguided dietary advice with which we have been bombarded over the past five decades, is an important contributing factor.

In fact, there has never been any scientific evidence that cutting down total fat consumption has any positive effect on health; specifically, reduced risks of heart disease and diabetes. For years those who pointed this out were marginalized, but recently evidence debunking the supposed benefits of low-fat diets has reached a critical mass, so that a mainstream magazine such as Time could write in 2014: “Scientists labeled fat the enemy. Why they were wrong.” And now the official Scientific Report of the 2015 Dietary Guidelines Advisory Committee admits that much.

There are several reasons why eating a low-fat diet is actually bad for your health. One is that if you lower the proportion of fat in your diet, you must replace it with something else. Eating more carbohydrates (whether refined or “complex”) increases your chances of becoming diabetic. Eating more proteins increases your chances of getting gout.

But perhaps a more important reason is that many Americans stopped eating food, and switched to highly-processed food substitutes: margarine, processed meats (such as the original Spam—not to be confused with email spam), low-fat cookies, and so on. In each case, we now have abundant evidence that these are “anti-health foods”, because they contain artificial trans fats, preservatives, or highly-processed carbohydrates.

While controlled diet studies are important and necessary for making informed decisions about our diets, an exciting recent scientific breakthrough has resulted from the infusion of evolutionary science into nutrition science. After all, you need first to figure out what hypotheses you want to test with controlled trials, and evolution turned out to be a fertile generator of theoretical ideas for such tests.

One of the sources of ideas to test clinically is the growing knowledge of the characteristic diets of early human beings. Consider this simple idea (although it clearly was too much for traditional nutritionists): we will be better adapted to something eaten by our ancestors over millions of years than to, say, margarine, which we first encountered only 100 years ago. Or take a food like wheat, to which some populations (those in the Fertile Crescent) have been exposed for 10,000 years, and others (Pacific Islanders) for only 200 years. Is it surprising that Pacific Islanders have the greatest prevalence of obesity in the world (higher even than in the United States)? And should we really tell them to switch to a Mediterranean diet, heavy on grains, pulses, and dairy, to which they’ve had no evolutionary exposure whatsoever?

Our knowledge of ancestral diets, of course, is itself evolving very rapidly. But it seems clear that we are adapted to eating a variety of fatty foods, including grass-fed ruminants (beef and lamb) and seafood (oily fish), both good sources of Omega-3 fatty acids. Of particular importance could be bone marrow—it is quite likely that first members of the genus Homo (e.g., habilis) were not hunters, but scavengers who competed with hyenas for large marrow bones. It’s very probable that nutrients from bone marrow (and brains!) of scavenged savannah ungulates were the key resource for the evolution of our own oversized brains.

In light of the new knowledge it is clear why Americans are getting fatter by eating low-fat diets. When you eliminate fatty foods that your body and—especially—brain need, your body will start sending you persistent signals that you are malnourished. So you will overeat on foods other than fatty ones. The extra, unnecessary calories that you consume (probably from carbohydrates) will be stored as fat. As a result, you will be unhappy, unhealthy, and overweight. You can avoid those extra pounds, of course, if you have a steely will (which few people have)—then you will not be overweight, merely unhappy and unhealthy.

So to lose fat you need to eat—not fat—but fatty foods. Paradoxically, eating enough fatty food of the right sorts will help to make you lean, as well as happy and—Edge readers, take note—smart!

It has been increasingly exciting to follow the recent surge in the discovery of exoplanets. Not only because what started as a needle in the haystack endeavor in the late 1980s has become a booming field of space exploration, when it gained its latest momentum with the success of Nasa’s Kepler Telescope. As of this moment of writing, the exoplanet data explorer maintained by Jason Wright at Penn State lists 1,642 confirmed planets and 3,787 unconfirmed Kepler candidates.

There are severe downsides to most of those planets. Only 63 light years away for example a blue marble planet named HD 189733b orbits its star. Daytime temperatures on this planet average 1,700 degrees Fahrenheit, wind speeds reach 7,000 miles per hour and the blue color in the atmosphere comes from rains of molten glass. Only four of the exoplanets found by now have the right distance to their stars to host life. Given the UC Berkeley estimate of 11 billion earth-like planets in the milky way alone, there is a definite conclusion though that our home planet is far from unique in the universe, even though we as humans are very much alone.

Most conclusions drawn from the discovery of exoplanets aren’t quite as philosophical. Great findings about the history of the universe and the origins of life are made. There’s even a practical side to it. Given that the search for exoplanets has enabled science to peer farther into the universe as ever before, short-distance space missions have been reduced to outer space prancings of status seeking enterprises and emerging nations trying to prove they are the new superpowers.

While the glamour of physical space exploration still lives on in the dreams of billionaires and potentates, even pop culture ideas of settling space are gaining traction again. With the apocalyptic specter of climate change rendering this planet inhabitable colonizing other planets seem like an attractive idea.

Blockbusters like Interstellar and The Martian have used this longing for a life beyond our atmosphere for great entertainment. But even when Harvard astronomer Dimitar Sasselov toured the conference circuit a few years ago talking about the thrill of discovering far away planets (his team had actually once found the furthest planet of them all), you could sense the pangs of science fiction longings in the audience. What if there indeed is life out there? Other habitable planets? They still lingered, no matter the thoroughly scientific nature of the research of exoplanets. After all, Sasselov also serves as the director of Harvard’s origins of life initiative.

But it is exactly those lingering science fiction dreams, which make the news about the vast number of exoplanets of important. As symbols they can serve as an extension of the Blue Marble image of planet earth taken by the Apollo 17 crew in December of 1972. Back then the Blue Marble showed mankind the reality of what Buckminster Fuller had called spaceship earth just four years before—earth being a rather small vehicle with finite resources. The Blue Marble went on to the cover of Stewart Brand’s Whole Earth Catalog, the principle manual of the emerging ecological movement.

Even though the recent wave of anachronistic space age glamour overshadows the great news about exoplanets, they are great symbols of a shift in global consciousness. With all escape routes now officially closing (planet HD 189733b being just one sensational example of the forbidding nature of space), the realization that mankind has to make the best out of its home planet starts to become common knowledge not only in progressive circles. Climate talks in Paris have shown that the political will to take action finally transcends borders, ideologies and national interests.

The symbolism of exoplanets goes beyond the Buckminster Fuller metaphor of spaceship earth. It shows that the drive of science knows no limits. When astronomers will confirm the first extragalactic planets, the reach for infinity will open even wider realms of understanding the universe. This understanding coupled with a new consciousness about the value and fragility of planet earth can lead to a will to push for solutions right here on earth. Science will continue to reach ever farther into infinity for ever great findings. Here on earth this will just strengthen the realization that the dream of habitable planets or even communicating life forms in our reach is as absurd as the ideas of afterlives and deities.

When prehistoric humans arrived in America they found a continent populated by mammoths, wooly rhinos, giant sloths, sabertooths, horses, and camels. By the time Columbus arrived, all of these species were extinct, due mainly to human hunting. But today, paleobiologists are sequencing the genomes of these extinct species. Furthermore, genetic engineering is approaching the point where genetically engineered versions of these extinct species may walk the Earth again. This year's big news—cloning of a dead pet dog—is thus merely a small beginning.

In the near future, the news will concern what paleo-DNA specialist Beth Shapiro dubs "de-extinction": generating living organisms bearing genes recovered from extinct species. Paleo-DNA (the somewhat degraded DNA recovered from bones or hair of extinct species) can be extracted from recently extinct species like mammoths and those exterminated by humans during historical times including passenger pigeons, dodos or thylacines (Jurassic Park fans note: the truly ancient DNA from dinosaurs is too degraded to currently allow sequencing). Trace amounts of DNA can be recovered, amplified and sequenced. Key genes could then be engineered into cells of the closest living relatives (Asian elephants for mammoths) to produce shaggy, cold tolerant elephants. This is nearly within technological reach today. Although birds pose unique challenges (being unclonable with current technologies), the de-extinction of passenger pigeons or moas (3.5 m tall flightless birds exterminated by hunting when the Maori arrived in New Zealand) appears within our technical grasp, and major de-extinction projects for these species are already underway.

So should we do it? Shapiro's recent book How to Clone a Mammoth provides an excellent introduction to the arguments. Given the polarized opinions generated by reintroducing wolves to Yellowstone, one can easily imagine diversity of public reactions to reintroducing sabertooth tigers or giant cave bears. I think that the best "pro" arguments are ecological—by reviving lost species we can restore habitats damaged or destroyed as our species spread over the planet. Mammoth-like elephants stomping through the tundra of Siberia's Paleozoic Park would benefit the environment by slowing the process of permafrost melting and the attendant carbon release. From a purely scientific, curiosity-driven viewpoint, de-extinction will offer biological and ecological insights available in no other way. Economically, tourists would pay top dollar to watch moas wandering the beech forests of New Zealand, or mammoths roaming Siberia. The con arguments are mostly practical (why spend money reviving extinct species rather than saving living endangered species?) or techno-fearful (humans shouldn't play god), but nonetheless passionately advocated.

By far the most significant issues will concern extinct hominids like Neanderthals, and society needs to prepare for the challenging ethical questions raised by such research. In 1997 researchers at Svante Pääbo's paleo-DNA lab in Leipzig made the news by sequencing mitochondrial DNA from Neanderthals. Today, after breathtaking technological progress, a full Neanderthal genome is available online. Even more exciting, in 2010 Pääbo's group discovered Denisovans, a previously-unknown Asian hominid species, based on DNA extracted from a tiny finger bone. The discovery of Denisovans from paleo-DNA makes it crystal clear that when modern humans emerged from Africa, they encountered a world inhabited by multiple near-human species—all of them now extinct.

Recovering the genome sequence of an extinct hominid species is exciting because it provides answers to a host of biological questions concerning which stones and bones (the previous mainstay of paleoanthropology) will remain forever mute. For example, it seems likely based on pigmentation genes that Neanderthals had light skin and some had red hair. Paleo-DNA has clarified that some interbreeding probably occurred when the first modern humans migrated out of Africa and encountered Neanderthals. As a result, all non-African human populations bear traces of Neanderthal DNA in their genomes (and many Asians bear additional Denisovan DNA). Similarly, the issue of whether Neanderthals had spoken language has divided scholars for decades. Although the case remains far from closed, we now know that Neanderthals shared the derived human version of the FOXP2 gene which enhances our speech motor control. This suggests that Neanderthals were able at least to produce complex vocalizations, even if they lacked modern, syntactic language. Such findings have fueled an ongoing sea-change in contemporary interpretations of Neanderthals—from oafish thugs to smart, resourceful near-humans.

Paleo-DNA sequencing has changed not only our understanding of Neanderthals, but of ourselves. Neanderthals were not modern humans: they lacked the rapid cultural progress characterizing our species, and thus presumably some of our cognitive capacities. But what precisely were these differences? Do these differences make us, the survivors, "human?" Or were Neanderthals human, but "differently abled?" Certainly, with the bodies of Olympic wrestlers and brains slightly larger than modern humans, they'd be first picks for your rugby scrum; perhaps they had unique cognitive abilities as well. Progress will be rapid in addressing these issues, because each new insight into the genetic basis of the human brain yields parallel insights into our understanding of Neanderthal brains—and of the cognitive differences between the two.

But the truly deep ethical issues concern the possible de-extinction of Neanderthals or other extinct hominids. From a scientific viewpoint this would promise insights into hominid evolution and human nature unimaginable a decade ago. But from a legal viewpoint it would involve creating humans expressing Neanderthal genes, and thus require human cloning already forbidden in many countries. But few doubt that, within this century, genetic engineering of our own species will be both technologically possible and ethically acceptable in at least some sub-cultures. Clearly, a human expressing Neanderthal genes (as many of us already do!) would retain all basic human rights, but the moral and ethical implications raised by Neanderthals in the workplace (or on college football teams) might easily eclipse those raised by racism or slavery.

Clearly paleo-DNA will remain in the news for the foreseeable future, offering scientific insights and posing unprecedented ethical quandaries. It will thus behoove all thinking people (especially politicians drafting legislation) to become acquainted with the technology and the biological facts before forming an opinion.

The most remarkable breaking news in science is that I exist. Well, not just me. People like me who, without technology, would have died early. Of the roughly 5 ½ billion people who survived past puberty, perhaps only one billion would be here were it not for modern sanitation, medicine, technology, and market-driven abundance. Ancestrally, the overwhelming majority of humans died before they had a full complement of children, often not making it past childhood. For those who live in developed nations, our remodeled lifetables are among the greatest of the humane triumphs of the Enlightenment—delivering parents from the grief of holding most of their children dead in their arms, or of children losing their parents (and then themselves dying from want).

But there is hidden and unwelcome news at the core of this triumph. This arises out of the brutal way natural selection links childbearing to the elimination of genetic disease.

The first thing to recall is that even our barest functioning depends on amazingly advanced organic technology at all scales—technology engineered by selection. For example, our eyes—macroscopic objects—have two million moving parts, and yet individual rods are so finely crafted they can respond to single photons. Successful parents in every species live near spiring summits on adaptive landscapes.

The second thing to remember is that physics is perpetually hurling us off these summits, assaulting the organization that is necessary to our existence. Entropy not only ages and kills us as individuals, but also successfully attacks each parent’s germ line. Indeed, the real news is that a number of methods have converged on the estimate that every human child contains roughly 100 new mutations—genetic changes that were not present in their parents. To be sure, many of these occur in inert regions, or are otherwise “silent” and so do no harm. But a few are very harmful individually; and although the remainder are individually small in effect, collectively they plague each individual with debilitating infirmities.

These recent estimates are striking when one considers how, in an entropy-filled world, we maintained our high levels of biological organization. Natural selection is the only physical process that pushes species’ designs uphill against entropy toward greater order (positive selection), or maintains our favorable genes against the downward pull exerted by mutation pressure (purifying selection). If a species is not to melt down under the hard rain of accumulating mutations, the rate at which harmful mutations are introduced must equal the rate at which selection removes them (mutation-selection balance). This removal is self-executing: Harmful genes cause impairments to the healthy design of the individuals they are situated in. These impairments (by definition) are characteristics that reduce the probability that the carrier will reproduce, and thereby reduce the number of harmful genes passed on. For a balance to exist between mutation and selection, a critical number of offspring must die before reproduction—die because they carry an excess load of mutations.

Over the long run, successful parents average a little more than two offspring that survive into parenthood. (The species would go extinct or fill the planet if the average were smaller or greater.) This is as true for humans with our handful of children as it is for an ocean sunfish with a nest full of 300 million. To understand how endlessly cruel the anti-entropic process of selection is, consider a sunfish mother with one nest. On average 299,999,997 of her progeny die, and 2 or 3 become comparable parents. Since the genotypes of offspring are generated randomly, the number of coin flips (in 300 million series) guarantees a lower end of the bionomial distribution of mutations that are many standard deviations out. That is, there are two or three who become parents because they received a set of genes that are improbably free of negative mutations. These parents therefore restart the lineage’s next generation having shed enough of the mutational load to have rolled back mutational entropy to the parental level. Ancestral humans, with far smaller offspring sets, maintained our functional organization more precariously, having survived over evolutionary time on the edge of a far smaller selective gradient between those children with somewhat smaller sets of impairments and those with somewhat larger sets. Most ancestral humans were fated by physics to be childless vessels whose deaths served to carry harmful mutations out of the species.

Now, along comes the demographic transition—the recent shift to lower death rates and then lower birth rates. Malthusian catastrophe was averted, but the price of relaxing selection has been moving the mutation-selection balance toward an unsustainable increase in genetic diseases. Various naturalistic experiments suggest this meltdown can proceed rapidly. (Salmon raised in captivity for only a few generations were strongly outcompeted by wild salmon subject to selection.) Indeed, it is possible that the drop in death rates over the demographic transition caused—by increasing the genetic load—the subsequent drop in birth rates below replacement: If humans are equipped with physiological assessment systems to detect when they are in good enough condition to conceive and raise a child, and if each successive generation bears a greater number of micro-impairments that aggregate into, say, stressed exhaustion, then the paradoxical outcome of improving public health for several generations would be ever lower birth rates. One or two children are far too few to shed incoming mutations.

No one could regret the victory over infectious disease and starvation now spreading across the planet. But we as a species need an intensified research program into germline engineering, so that the Enlightenment science that allowed us to conquer infectious disease will allow us to conquer genetic disease (through genetic repair in the zygote, morula, or blastocyst). With genetic counseling, we have already focused on the small set of catastrophic genes, but we need to sharpen our focus on the extremely high number of subtle, minor impairments that statistically aggregate into major problems.

I am not talking about the ethical complexities of engineering new human genes. Imagine instead that at every locus, the infant received healthy genes from her parents. These would not be genetic experiments with unknown outcomes: Healthy genes are healthy precisely because they interacted well with each other over evolutionary time. Parents could choose to have children created from their healthiest genes, rather than leaving children to be shotgunned with a random and increasing fraction of damaged genes. Genetic repair would replace the ancient cruelty of natural selection, which only fights entropy by tormenting organisms because of their genes.

Thomas Hobbes’s uncomfortable view of human nature looks remarkably prescient in the light of new discoveries in Kenya. Back in the mid 17th century—before anyone had any inkling of deep time or the destabilization of essential identity that would be brought about by an understanding of the facts of human evolution (i.e. before the idea was possible that nature was mutable)—Hobbes argued that we were fundamentally beastly (selfish, greedy, cruel) and, in the absence of certain historically-developed and carefully nurtured institutional structures, would regress to live in a state of nature, in turn understood as a state of perpetual war.

We can assume that John Frere would have agreed with Hobbes. Frere, we may read (and here Wikipedia is orthodox, typical and, in a critical sense, wrong), “was an English antiquary and a pioneering discoverer of Old Stone Age or Lower Paleolithic tools in association with large extinct animals at Hoxne, Suffolk in 1797.” In fact, while Frere did indeed make the first well-justified claim for a deep-time dimension to what he carefully recorded in situ, saying that the worked flints he found dated to a “very remote period indeed,” he did not think they were tools in any neutral sense, stating that the objects were “evidently weapons of war, fabricated and used by a people who had not the use of metals.”

Frere’s sharp-edged weapons can now be dated to Oxygen Isotope Stage 11—in other words, to a period lying between 427 and 364 thousand years ago—and even he might have been surprised to learn that the people responsible were not modern humans, but a species, Homo erectus, whose transitional anatomy would first come to light through fossil discoveries in Java a century later. Subsequent archaeological and palaeoanthropological work (significant aspects of it pioneered by Frere’s direct descendant, Mary Leakey) has pushed the story of genus Homo back ever further, revealing as many as a dozen distinct species (the number varies with the criteria used).

Alongside the biological changes runs a history, or prehistory, of technology. It has usually been supposed that this technology, surviving mainly as modified stone artifacts, was a product of the higher brain power that our human ancestors displayed. According to Darwin’s sexual selection hypothesis, female hominins favored innovative male hunters, and the incremental growth in intelligence led, ultimately, to material innovation (hence the evocative but not taxonomic term, Homo faber—Man the Maker).

So powerful was this idea that, although chipped stone artifacts dating to around 2.6 million years ago have been known for some time, there was a strong presumption that genus Homo had to be involved. This is despite the fact that the earliest fossils with brains big enough to be classified in this genus dated at least half a million years later. It was a fairly general hunch within palaeoanthropology that the genus Homo populations responsible for early chipped-stone technologies had simply not yet been discovered. Those few of us who, grounded in the related field of theoretical archaeology, thought differently, remained reliant on a very broad kind of consilience to counter this ex silencio assumption.

So to me it was wonderful news when, in May 2015, Sonia Harmand and co-workers published an article in Nature entitled “3.3-million-year-old stone tools from Lomekwi 3, West Turkana, Kenya,” because the strata at their site date to a period when no one seriously doubts that australopithecines, with their chimp-sized brains, were the smartest of the savannah-dwelling hominins. The discovery shows unambiguously that technology preceded, by more than one million years, the expansion of the cranium that is traditionally associated with the emergence of genus Homo.

Harmand et al follow current convention in naming their artifacts tools, rather than offensive weapons. This apparently more neutral terminology is, I think, a reflex of our continual and pervasive denial of Hobbes’s truth. Hobbes obviously did not have the information available to understand the time-depths or the bio-taxonomic and evolutionary issues involved, but had he known about Lomekwi 3 he would probably have called it differently, as Frere subsequently did at Hoxne.

The pre-human emergence of weapons technology makes functional and evolutionary sense. We are not talking about choosing appropriate twigs for the efficient extraction of termites, or the leaves best suited to carrying water, but about the painstaking modification of fine-grained igneous rocks into sharp-edged or bladed forms whose principal job was to part flesh from flesh and bone from bone.

If the word hunting helps gloss the reality of what such sharp stone artifacts were used for, then we can reflect on the type of hunting recently documented from the Aurora stratum at the site of Gran Dolina (Atapuerca), Spain. Here, Palmira Saladie Balleste and co-workers recently reported (Journal of Human Evolution, 2012) on a group of Homo antecessor (or, arguably, erectus) who fed on individuals from, presumably, a rival group. The age profile of the victims was “similar to the age profiles seen in cannibalism associated with intergroup aggression in chimpanzees,” that is, those eaten were infants and immature individuals.

That much of the data on early, systematic, endemic violence in our deeper (and shallower) prehistory comes as a surprise is due to the pervasive myth about the small-scale band societies of the archaeological past having somehow been egalitarian. This idea has an odd genealogy that probably goes back to the utopian dreaming of Hobbes’s would-be intellectual nemesis, Jean-Jacques Rousseau, with his ideas of harmony and purity in nature, and his belief that the savage state of humanity was noble rather than decadent.

Rousseau, like Hobbes, lacked the possibility of an evolutionary perspective, and also dealt in more or less essentialist assertions. But once we have to bridge from wild primate groups to early human ancestors, the tricky question of the roots of egality arises. If the chimpanzees and gorillas that can be studied in the wild have clear status hierarchies that are established, maintained and altered by force, including orchestrated murder and cannibalism, then how could fair play magically become a base-line behavior?

Somewhere along the way, it seems to have been forgotten that the 19th century founders of modern socio-cultural anthropology, Lewis Henry Morgan, Edward Burnett Tylor, and Edvard Westermarck, recorded all kinds of unfairness in indigenous North American and Pacific societies: we may not want the Tlingit and Haida, Ojibwa and Shawnee to have had slaves, for instance, but avoiding mentioning it in modern textbooks does a great disservice to the original ethnographic accounts (and the slaves).

Returning to the news from Lomekwi 3, we can now see that technology was not just a figural but a literal arms race. Reanalyzing the take-off in cranial capacity that began around 2 million years ago, we can see that blades and choppers needed to be already available to replace the missing biology—the massive ripping canines and heavy jaw muscles that previously hampered, in direct bio-mechanical terms, the expansion of the braincase. As Frere would immediately have understood, these artifacts included weapons, perhaps predominantly and pre-eminently. Only by postulating high-level competition between groups can we understand the dramatic adaptive radiation of hominin types and the fact that, ultimately, only one hominin species survived.

The paradox is that, by sharpening the first knives to extend the range of possible forms of aggression, we opened up a much broader horizon, in which technology could be used for undreamed-of purposes. Yet Hobbes’s instinct that our nature was borne of war, and Frere’s conclusion that the world’s primal technology was offensive, should not be ignored. In the artificial lulls when atavism is forced into abeyance we are happy to forget Hobbes’s admonition that it is only through the careful cultivation of institutions that stable peace is at least possible.

In a great year for science it is hard to restrict applause to just one area, but a worthy one is cancer research, which has seen a number of advances. Genetic manipulation has rapidly reversed colorectal cancer in mice, a Dutch team has developed a highly accurate blood test for cancer, a general cure for cancer is promised by the discovery that attaching malaria proteins to cancerous cells destroys them, the Mayo Clinic has found a way of short-circuiting cancer cell growth by using a certain junction protein (PLEKHA7), early diagnosis of pancreatic cancer looks more possible following the identification of a protein on particles released by cancer cells in the pancreas, low-toxicity nano-pills for treatment of breast cancer look to be on the way, and the FDA has approved Palbociclid for breast cancer treatment. There may have been other announcements in the oncology field this year, but the cumulative effect of these developments would appear to support the claim made by a leading oncologist that within a generation no one under the age of eighty will die of cancer.

Falling in love activates the same basic brain system for wanting (the Reward System—specifically the mesolimbic dopamine pathway), as do all drugs of abuse, including addiction to heroin, cocaine, alcohol, and nicotine. Because this central neural network becomes active when addicted to any drug of abuse, I have long wondered whether feelings of romantic love can smother a drug craving; whether a drug craving can smother feelings of romantic love instead; or whether these two very different cravings might even work together—sensitizing this brain network to make the drug addict more receptive to romantic love and/or make the lover more prone to other forms of addiction. In short: how does this central brain system accommodate two different cravings at once?

All of these questions are still largely unanswered. But in 2012 a new article has made some inroads into this conundrum. In this study, Xiaomeng Xu and her colleagues put eighteen Chinese nicotine-deprived smokers who had also just fallen madly in love into a brain scanner, using functional magnetic resonance imaging (fMRI). As these men and women looked at a photo of a hand holding a cigarette and also at a photo of their newly beloved, the researchers collected data on their brain activity. Results? Among those who were moderately addicted to nicotine, the craving for the beloved reduced activity in brain regions associated with the craving for a cigarette.

But there is some added value here. The article also suggests that engaging in any kind of novel activity (unrelated to romance) may also alleviate nicotine craving—by hijacking this same dopaminergic reward system. This single correlation could be of tremendous value to those trying to quit smoking.

And I shall go out on a limb to propose a wider meaning to these data. Although there is only this very limited evidence for my hypothesis, this study suggests to me that there may be a hierarchy to the addictions. In this case, one’s addiction to a newly beloved may, in some cases, suppress one’s addiction to nicotine. Romantic love may be the mother of all addictions—indeed a positive addiction that enables one to overcome other cravings to win life’s greatest prize: a mating partner.

In 2014 a group of big data scientists (including myself), representatives of big data companies, and the heads of National Statistical Offices from nations in both the northern and southern hemispheres, met within the United Nations headquarters and plotted a revolution. We proposed that all of the nations of the world begin to measure poverty, inequality, injustice, and sustainability in a scientific, transparent, accountable, and comparable manner. Surprisingly, this proposal was approved by the UN General Assembly in 2015, as part of the 2030 Sustainable Development Goals.

This apparently innocuous agreement is informally known as the Data Revolution within the UN, because for the first time there is an international commitment to discover and tell the truth about the state of the human family as a whole. Since the beginning of time, most people have been isolated, invisible to government and without information about or input to government health, justice, education, or development policies. But in the last decade this has changed. As our UN Data Revolution report, titled “A World That Counts,” states:

Data are the lifeblood of decision-making and the raw material for accountability. Without high-quality data providing the right information on the right things at the right time, designing, monitoring and evaluating effective policies becomes almost impossible. New technologies are leading to an exponential increase in the volume and types of data available, creating unprecedented possibilities for informing and transforming society and protecting the environment. Governments, companies, researchers and citizen groups are in a ferment of experimentation, innovation and adaptation to the new world of data, a world in which data are bigger, faster and more detailed than ever before. This is the data revolution.

More concretely, the vast majority of humanity now has a two-way digital connection that can send voice, text, and most recently, images and digital sensor data because cell phone networks have spread nearly everywhere. Information is suddenly something that is potentially available to everyone. The Data Revolution combines this enormous new stream of data about human life and behavior with traditional data sources, enabling a new science of “social physics” that can let us detect and monitor changes in the human condition, and to provide precise, non-traditional interventions to aid human development.

Why would anyone believe that anything will actually come from a UN General Assembly promise that the National Statistical Offices of the member nations will measure human development openly, uniformly, and scientifically? It is not because anyone hopes that the UN will manage or fund the measurement process. Instead we believe that uniform, scientific measurement of human development will happen because international development donors are finally demanding scientifically sound data to guide aid dollars and trade relationships.

Moreover, once reliable data about development start becoming familiar to business people, we can expect that supply chains and private investment will begin paying attention. A nation with poor measures of justice or inequality normally also has higher levels of corruption, and a nation with a poor record in poverty or sustainability normally also has a poor record of economic stability. As a consequence, nations with low measures of development are less attractive to business than nations with similar costs but better human development numbers.

Historically we have always been blind to the living conditions of the rest of humanity; violence or disease could spread to pandemic proportions before the news would make it to the ears of central authorities. We are now beginning to be able to see the condition of all of humanity with unprecedented clarity. Never again should it be possible to say “we didn’t know.” No one should be invisible. This is the world we want—a world that counts.

Research in developmental and comparative psychology has discovered that the humble pointing gesture is a key ingredient for the capacity to develop and use human language, and indeed for the very possibility of human social interaction as we know it.

Pointing gestures seem simple. We use them all the time. I might point when I give someone directions to the station, when I indicate which loaf of bread I want to buy, or when I show you where you have spinach stuck in your teeth. We often accompany such pointing gestures with words, but for infants who are not yet able to talk these gestures can work all on their own.

Infants begin to communicate by pointing at about nine months of age, a year before they can produce even the simplest sentences. Careful experimentation has established that prelinguistic infants can use pointing gestures to ask for things, to help others by pointing things out to them, and to share experiences with others by drawing attention to things that they find interesting and exciting.

Pointing does not just manipulate the other’s focus of attention, it momentarily unites two people through a shared focus on something. With pointing, we do not just look at the same thing, we look at it together. This is a particularly human trick, and it is arguably the thing that ultimately makes social and cultural institutions possible. Being able to point and to comprehend the pointing gestures of others is crucial for the achievement of “shared intentionality,” the ability to build relationships through the sharing of perceptions, beliefs, desires, and goals.

Comparative psychology finds that pointing (in its full-blown form) is unique to our species. Few non-human species appear to be able to comprehend pointing (notably, domestic dogs can follow pointing while our closest relatives among the great apes cannot), and there is little evidence of pointing occuring spontaneously between members of any species other than our own. It appears that only humans have the social-cognitive infrastructure needed to support the kind of cooperative and prosocial motivations that pointing gestures presuppose.

This suggests a new place to look for the foundations of human language. While research on language in cognitive science has long focused on its logical structure, the news about pointing suggests an alternative: that the essence of language is found in our capacity for the communion of minds through shared intentionality. At the center of it is the deceptively simple act of pointing, an act that must be mastered before language can be learned at all.

Powerful computation today boosts our capacity to perceive and understand the world. The more data we process, analyze, and visualize, the more natural and social phenomena we discover and understand.

Computational capacities allow us processing and visualizing massive data produced by microscopes, telescopes, and satellites. As a result, today we perceive, understand, and forecast “new” natural objects and characteristics, from nano-bots to distant galaxies and climates.

In the same sense, processing massive social data reveals global trends. For instance, visualizing and analyzing massive judicial information with current computational tools reveal a whole new and complex social phenomenon: Macro-criminal networks.

Our brain only makes sense of social networks in which approximately 150 – 200 individuals participate. Known as the “Dunbar’s number,” this is an approximation of the social network’s size that we can interact with; it’s almost impossible for our brains to understand social networks articulated by several hundreds or thousands of individuals.

Therefore, macro-criminal networks are complex social structures that cannot be perceived or analyzed without computational power, algorithms, and the right concepts of social complexity.

Unfortunately, these global, resilient, and decentralized structures, characterized by messy hierarchies and various types of leaders, bring bad news: we, as a society, lack tools, legislation, and enforcement mechanisms to confront them.

Macro-criminal networks overwhelm most judicial and enforcement agents who still fight crime searching for “criminal organizations” with simple hierarchies, articulated by “full time” criminals, and commanded by a single boss. This classic idea of “organized crime” is outdated and doesn’t reflect the complexity of macro-criminal networks that are being discovered.

Since crime in most countries is analyzed and confronted through outdated legislation, methodologies, and concepts, these huge criminal networks are usually omitted. The outdated approach restricts judicial systems and enforcement agencies to observe a tiny part of crime.

Investigating and prosecuting crime today without the right concepts and without computational tools for processing, analyzing, and visualizing massive data, is like studying galaxies with 17th century telescopes and without computers. In this sense, the hardest challenge when confronting macro-criminal networks is not adopting powerful computers or applying deep learning but modifying that mindset of scholars, investigators, prosecutors and judges.

For instance, legislation focused on one victim and one victimizer, conducts to wrong analysis and insufficient enforcement of systemic crimes, such as massive corruption observed in Latin America and West Africa, human trafficking observed in Eastern Europe, and massive forced displacement observed in Central Africa. As a consequence, structures supporting those crimes worldwide are overlooked.

Crime in its various expression is always news. From corruption to terrorism and several types of trafficking activities, crime affects our way of life while hampering development in various countries. However, computational power today reveals the—bad—news of huge, resilient, and decentralized criminal macro-structures. Understanding this phenomenon and its related concepts and consequences, is critical for achieving global security in years to come. It is important to commit and allocate the right scientific, institutional, and economic resources to deal with this transnational phenomenon that we just recently understand, although it today underlies the evolution of various countries.

2015 saw the publication of an impressive tour d’horizon of global ecology. Covering many areas, it assesses human impacts on the loss of biodiversity, the subject that falls within my expertise. Like all good reviews, it’s well documented, comprehensive, and contains specific suggestions for future research. Much of it has a familiar feel, though it’s a bit short on references from Nature and Science. But that’s not what makes this review news. Rather, it’s because it reached a well-defined 1.2 billion people around plus uncountable others. That’s a billion with a “b,” likely putting the citation impact statistics of other recent science stories into the shade. The publication is “On care for our common home” and its author is better known as Pope Francis.

How much ecology is there in this? And how good is it? Well, the word “ecology” (or similar) appears eighty times, “biodiversity” twelve, and “ecosystem” twenty-five. There’s a 1400 word section on the loss of biodiversity—the right length for a letter to Nature.

The biodiversity section starts with a statement that the “earth’s resources are also being plundered because of short-sighted approaches to the economy, commerce and production.” It tells us that deforestation is a major driver of species loss. As for the importance of the topic, it explains why a diversity of species are import as sources of food, medicines and other uses, while “different species contain genes which could be key resources in years ahead for meeting human needs and regulating environmental problems.” A high rate of extinction raises ethical issues, particularly those involving where our current actions limit what future generations can use or enjoy.

We learn that most of what we know about extinction comes from studying birds and mammals. In a sentence that E.O. Wilson might have written, it praises the small things that rule the world. “The good functioning of ecosystems also requires fungi, algae, worms, insects, reptiles and an innumerable variety of microorganisms. Some less numerous species, although generally unseen, nonetheless play a critical role in maintaining the equilibrium of a particular place.”

There is no point in a complete catalogue, but this short list exemplifies its insights and comprehensiveness.

Knocking pieces from any complex system—in this case species from ecosystems—can have unexpected effects.

Technology has benefits, but the idea of unbridled technological optimism was my Edge choice two years ago of an “idea that must die.” Bergoglio expresses that eloquently with “We seem to think that we can substitute an irreplaceable and irretrievable beauty with something which we have created ourselves.”

We not only destroy habitats, but we massively fragment those that remain behind. The solution is to create biological corridors.

“When certain species are exploited commercially, little attention is paid to studying their reproductive patterns in order to prevent their depletion and the consequent imbalance of the ecosystem.”

There has been significant progress in establishing protected areas on land and in the oceans.

There are concerns about the Amazon and the Congo, the last remaining large blocks of tropical forests. There are concerns about replacing native forests with tree plantations that are so much poorer in species.

Overfishing and discarding large amounts of bycatch diminish the oceans’ ability to support fisheries. Human actions physically damage the seabed across vast areas, massively changing the composition of the species that live there.

It ends with a statement that might be from a Policy Forum in Science arguing as it does for increased effort and funding.

Greater investment needs to be made in research aimed at understanding more fully the functioning of ecosystems and adequately analysing the different variables associated with any significant modification of the environment. Because all creatures are connected, each must be [conserved], for all … are dependent on one another. … This will require undertaking a careful inventory of the species which it hosts, with a view to developing programmes and strategies of protection with particular care for safeguarding species heading towards extinction.

In this, I’ve changed only the word “cherished” to “conserved.”

Reading the biodiversity section again, having just finished teaching my graduate conservation class, made me think it would make an outstanding course outline next year. Its coverage is impressive, its topics of global significance. Its research is strikingly up to date and hints at very active controversies. To answer my questions: yes, there is lots of ecology and it’s of a very high order, too.

The publication’s other lengthy sections include sections on pollution, climate change, water, urbanization, social inequality and its environmental consequences, both the promise and threat of technology, inter-generational equity, policies both local and global. All these topics would appear in a course on global ecology. This is not why the publication made news.

Rather, it’s an incontestable statement of the importance of science in shaping what are the ethical choices of our generation—both for Catholics and those of us who are not. It appeals for all religions and all scientists to grasp this enormity of the problems that the science of ecology has uncovered and to seek their solutions urgently. The author deserves the last word—and it is a good one—on how we should do that:

Nonetheless, science and religion, with their distinctive approaches to understanding reality, can enter into an intense dialogue fruitful for both. Given the complexity of the ecological crisis and its multiple causes, we need to realize that the solutions will not emerge from just one way of interpreting and transforming reality. ... If we are truly concerned to develop an ecology capable of remedying the damage we have done, no branch of the sciences and no form of wisdom can be left out, and that includes religion and the language particular to it.

It may not be an exaggeration to say that within the field of medicine the most progress made in the last few decades is in the field of clinical imaging: starting with simple X-rays to computerized axial tomography (CT scan or CAT scan), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET scan), single photon emission computed tomography (SPECT scan), nuclear tagged scanning such as Ventilation/Perfusion scan to rule out blood clots in the lung (V/Q scan). And then there is Ultrasonography, which has been extensively used in diagnostic and also therapeutic interventions in many body cavities such as amniocentesis during pregnancy, or drainage of an inflamed gal bladder or evaluating kidneys for stones, or for evaluation of arteries and veins, etcetera.

Ultrasonography is also used extensively in imaging of the heart (Echocardiography), and is used for M-mode, 2D or 3D imaging. Cardiologists have used various imaging modalities for diagnosis of heart conditions. These include echocardiography as mentioned above, diagnostic heart catheterization in which a catheter is passed from the groin into the heart via the Femoral artery or vein, while watching the progress under x-ray. They perform contrast studies by injecting radio-opaque material in the heart chambers or blood vessels while recording moving images (angiograms). And then there is Computed Tomographic Angiography of the heart (CTA) with its 3-D reconstruction, which provides detailed information of the cardiac structure (Structural Heart Imaging).

Now comes 3D printing, adding another dimension to imaging of human body. In its current form, using computer aided design (CAD programs), engineers develop a three-dimensional computer model of any object to be “printed,” (or built), which is then translated into a series of two-dimensional “slices” of the object. The 3D Printer can then “print” or lay thousands of layers (similar to ink or toner onto paper in a 2D printer) until the vertical dimension is achieved and the object is built.

Within the last few years this technology has been utilized in the medical field, particularly in surgery. It is another stage in advancement of “imaging” of the human body. In the specialty of cardiac surgery, 3D printing is being applied mostly in congenital heart disease. In congenital heart malformations, many variations from the normal can occur. With current imaging techniques, surgeons have a fair idea as to what to expect before going to operate, but many times they have to “explore” the heart at surgery to really find out the exact malformation and then plan the operation at the spur of the moment. With the advent of 3D printing, one can do a CTA scan of the heart with its three-dimensional reconstruction, which can then be fed into the 3D printer and a model of the malformed heart can be created. The surgeons can then study this model and even cut slices into it to plan the exact operation they will perform and save valuable time during the procedure itself.

Three-dimensional printing is being applied in many areas of medicine, particularly in orthopedics. One of the more exciting areas is in use of 3D printing for making live organs for replacements using living cells and stem cells layered onto scaffolding of the organ to be “grown,” so the cells can grow into skin, ear lobe or other organs. One day in the future, organs may be grown for each individual, from his/her own stem cells, obviating the risk of rejection and avoiding the poisonous anti-rejection medicines. Exciting development.

University Distinguished Professor of Psychology, Northeastern University; Research Neuroscientist, Massachusetts General Hospital; Lecturer in Psychiatry, Harvard Medical School; Author, How Emotions Are Made

Your brain is predictive, not reactive. For many years, scientists believed that your neurons spend most of their time dormant and wake up only when stimulated by some sight or sound in the world. Now we know that all your neurons are firing constantly, stimulating one another at various rates. This intrinsic brain activity is one of the great recent discoveries in neuroscience. Even more compelling is what this brain activity represents: millions of predictions of what you will encounter next in the world, based on your lifetime of past experience.

Many predictions are at a micro level, predicting the meaning of bits of light, sound, and other information from your senses. Every time you hear speech, your brain breaks up the continuous stream of sound into phonemes, syllables, words, and ideas by prediction. Other predictions are at the macro level. You’re interacting with a friend and, based on context, your brain predicts that she will smile. This prediction drives your motor neurons to move your mouth in advance to smile back, and your movement causes your friend’s brain to issue new predictions and actions, back and forth, in a dance of prediction and action. If predictions are wrong, your brain has mechanisms to correct them and issue new ones.

If your brain didn’t predict, sports couldn’t exist. A purely reactive brain wouldn’t be fast enough to parse the massive sensory input around you and direct your actions in time to catch a baseball or block a goal. You also would go through life constantly surprised.

The predictive brain will change how we understand ourselves, since most psychology experiments still assume the brain is reactive. Experiments proceed in artificial sequences called “trials,” where test subjects sit passively, are presented with images, sounds, words, etc., and make one response at a time, say, by pressing a button. Trials are randomized to keep one from affecting the next. In this highly controlled environment, the results come out looking like the subject’s brain makes a rapid, automatic response, followed by a controlled choice about 150 milliseconds later, as if the two responses came from distinct systems in the brain. These experiments fail to account for a predicting brain, which never sits awaiting stimulation, but continuously prepares multiple, competing predictions for action and perception, while actively collecting evidence to select between them. In real life, moments or “trials” are never independent because each brain state influences the next. Most psychology experiments are therefore optimized to disrupt the brain’s natural process of prediction.

The predictive brain presents us with an unprecedented opportunity for new discoveries about how a human brain creates a human mind. New evidence suggests that thoughts, feelings, perceptions, memories, decision-making, categorization, imagination, and many other mental phenomena, which historically are treated as distinct brain processes, can all be united by a single mechanism, prediction. Even our theory of human nature is up for grabs, as prediction deprives us of our most cherished narrative: the epic battle between rationality and emotions to control behavior.

Suppose you have just popped one of those new hedonic enhancement pills for virtual environments. Not the dramatic, illegal stuff they now discuss at erowid.org—that’s way too dangerous. No, just the legal pharmaceutical enhancement that came as a free direct-to-consumer advertising gift with the gadget itself. It has the great advantage of blocking nausea and thereby stabilizing the real-time, fMRI-based neurofeedback-loop into your own virtual reality (allowing you to interact with the unconscious causes of your own feelings directly, as if they were now part of an external environment), while at the same time nicely minimizing the risk of depersonalization disorder and Truman Show delusion. Those pills also reliably prevent addiction and the diminished sense of agency upon re-entering the physical body following long-time immersion—at least the package leaflet says so. As you turn on the device, two of your "Selfbook-friends" are already there, briefly flashing their digital subject identifiers. Their avatars immediately make eye contact and smile at you, and you automatically smile back while you feel the pill taking effect. Fortunately, they can see neither the new Immersive Porn trial version nor the expensive avatar that represents your Compassionate Self. You only use that twice a week in your psychotherapy sessions. The NSA, however, sees everything.

2016 will be the year in which VR finally breaks through at the mass consumer level. What is more, users will soon be enabled to toggle between virtual, augmented, and substitutional reality, experiencing virtual elements intermixed with their “actual” physical environment or an omnidirectional video feed giving them the illusion of being in a different location in space and/or time, while insight may not always be preserved. Oculus Rift, Zeiss VR One, Sony PlayStation VR, HTC Vive, Samsung’s Galaxy Gear VR or Microsoft’s HoloLens are just the very beginning, and it is hard to predict the psychosocial consequences over the next two decades, as an accelerating technological development will now be driven by massive market forces—and not by scientists anymore. There will be great benefits (just think of the clinical applications) and a host of new ethical issues ranging from military applications to data protection (for example, “kinematic fingerprints” generated by motion capture systems or avatar ownership and individuation will become important questions for regulatory agencies to consider).

The real news, however, may be that the general public will gradually acquire a new and intuitive understanding of what their very own conscious experience really is and what it always has been. VR is the representation of possible worlds and possible selves, with the aim of making them appear as real as possible—ideally, by creating a subjective sense of “presence” in the user. Interestingly, some of our best theories of the human mind and conscious experience describe it in a very similar way: Leading theoretical neurobiologists like Karl Friston and eminent philosophers like Jakob Hohwy and Andy Clark describe it as the constant creation of internal models of the world, virtual neural representations of reality which express probability density functions and work by continuously generating hypotheses about the hidden causes of sensory input, minimizing their prediction error. In 1995, Finnish philosopher Antti Revonsuo already pointed out how conscious experience exactly is a virtual model of the world, a dynamic internal simulation, which in standard situations cannot be experienced as a virtual model because it is phenomenally transparent—we “look through it” as if we were in direct and immediate contact with reality. What is historically new, and what creates not only novel psychological risks but also entirely new ethical and legal dimensions, is that one virtual reality gets ever more deeply embedded into another virtual reality: The conscious mind of human beings, which has evolved under very specific conditions and over millions of years, now gets causally coupled and informationally woven into technical systems for representing possible realities. Increasingly, consciousness is not only culturally and socially embedded, but also shaped by a specific technological niche that, over time, quickly acquires rapid, autonomous dynamics and ever new properties. This creates a complex convolution, a nested form of information flow in which the biological mind and its technological niche influence each other in ways we are just beginning to understand.

The field of psychology is experiencing a crisis. Our studies do not replicate. When Science published the results of attempts to replicate 100 studies, results were not confidence-inspiring, to say the least. The average effect sizes declined substantially, and while 97% of the original papers reported significant p values, only 36% of the replications did.

The same difficulty in reproducing findings is found in other scientific fields. Psychology is not alone. We know why so many studies that don’t replicate were published in the first place – because of the intense pressure to publish in order to get tenure, get grants, and teach fewer courses, and because of journals’ preference for publishing counterintuitive findings over less surprising ones. But it is worth noting that one-shot priming studies are far more likely to be flukes than longitudinal descriptive studies (e.g., studies examining changes in language in the second year of life) and qualitative studies (e. g., studies in which people are asked to reflect on and explain their responses and those of others).

In reaction to these jarring findings, journals are now changing their policies. No longer will they accept single studies with small sample sizes and p values hovering just below .05. But this is just the first step. Because new policies will result in fewer publications per researcher, universities will have to change their hiring, tenure, and rewards systems, and granting and award-giving agencies will have to do so too. We will need to stop the lazy practice of counting publications and citations and instead read critically for quality. That takes time.

Good will come of this. Psychology will report findings that are more likely to be true, less likely to lead to urban myths. This will enhance the reputation of psychology and more important our understanding of human nature.

The Blue Marble was the first full photograph of the Earth from space. The Apollo 17 mission took it on December 7, 1972 – over forty years ago. Of course, it was not the first time that a photograph of the planet had been shown widely. By then for example, the first image of the Earth as seen from the moon had been circulated. In 1969 the astronauts of the lunar landing had taken the famous shot of “Earthrise”, capturing the solitary fragility of our planet as it rises from darkness. But the blue marble represented a photograph of a different nature. It was comprehensive in scope yet detailed in nature, giving it an unusually high density of information and a powerful evocative quality – a symbol for the beginning of the Anthropocene. It was in fact during the seventies that man began recognizing its role as a member of a planetary ecosystem, and began wrestling with the question of its own impact on a surprisingly finite and vulnerable planet.

The Blue Marble shows the planet from the Mediterranean to Antarctica, with the African continent and the Arabian Peninsula in the foreground, and the Indian subcontinent and the Southern Ocean as frames. It provides an integrated single view of the planet’s atmosphere in its spellbinding complexity: the inter-tropical convergence zone, where moist air flowing equator-ward from North and South, rises in a narrow band of convective plumes that give the characteristic thunderous rainy weather to the tropics; the Sahara and Kalahari deserts around thirty north and south of the Equator, where that same air subsides from its pole-ward flow, drying out any remaining moisture as it completes the cycle of the Hadley cell, the atmospheric overturning circulation spanning the Tropics; a tropical cyclone, fed by the warm surface waters of the Arabian Sea is visible in the top right quadrant; the mid-latitude weather systems of the roaring forties over the Southern Ocean, marked by visible fronts, altocumulus, cirrocumulus clouds; the contour of Antarctica revealed in full view of the sun. A compendium of the Earth’s climate in a single shot.

Iconic geographic images can reframe how we conceive of our place on the planet. They are a recurring cultural phenomenon and a moment of synthesis that reveal the preoccupations of those who produced them. While we cannot know how widespread its adoption was, the first known map of the world – the 6th Century BC “Imago Mundi” from Mesopotamia – shows the city of Babylon as it relates to the surrounding cities, the Euphrates, and the Persian Gulf, synthesizing in one image the primary elements of threat and survival of an entire civilization. The “Peutinger Map”, almost ten centuries later, revealed the extent of the Roman state in one image, providing a map organized around the great land routes that represented the strength of Roman logistics and that had connected the empire. A different type of picture made the headlines around the world in 2015, one that is equally representative of our modern preoccupation with our own sustainability, and evocative of the challenges ahead.

“Tom and Jerry” went up in space on March 17, 2002. Two identical satellites formed the basis for the Gravity Recovery and Climate Experiment, GRACE for short. The satellites orbit the Earth 16 times every day at over 500 km altitude, sending back a map of the distribution of mass on the planet due to the variation in distribution of rocks and water. They produce this data by measuring distortions in the gravitational field caused by slight differences in the distribution. As the two satellites pass over these differences, the first is slightly accelerated or decelerated compared to the second. By measuring their relative distance to an astonishing level of accuracy – the satellites can detect a micron difference over two hundred kilometers – they provide an integrated, point-wise measurement of the gravitational field of the planet – a planetary CT scan of sorts. It is one of the great successes of modern geodesy that the resulting measurement can be inverted, filtered and analyzed to reveal the complex three-dimensional structure of the Earth.

One of the crucial applications of this technology has been to diagnose groundwater storage in the great aquifers of the world. Water is of course of a different density to the surrounding rock, thus leading to slight effects on the gravitational field, which GRACE can detect. Global hydrological assessments have always been hampered by the complex and local nature of the resource, making syntheses of the state of the world difficult to construct and of limited utility. When they do exist, they tend to be encyclopedic in nature, often published in ponderous, static volumes, listing individual rivers and nations as chapters, and based on single numbers, painstakingly inferred, compiled or estimated from sparse measurements and models: hardly an evocative synthesis to stimulate a humanist debate on our place on the planet.

But over the course of almost a decade and half since its launch, the long datasets offered by GRACE have provided the first integrated image of the state of groundwater resource use, an image global in scope, yet local in nature: a detailed, real-time diagnostic of the planet. And GRACE has shed light on the most obscure part of the water cycle – that which is hidden underneath the earth’s surface – and this is the picture that has made the news. It shows that one in three large aquifers in the world appear to be stressed, depleted by people drawing water for human use. It shows that California’s Central Valley, the Arabian Peninsula, the Indus basin all share a common fate, uniting vastly different economies and societies in a planetary challenge: our persistent inability to manage finite resources.

While scientific practice will be integrated rather than dramatically changed by GRACE’s data – remote sensing still requires significant processing, and integration with land-based measurements, to be operationally useful – the resulting images have already started to change the narrative on sustainability. That is why this data has made news in 2015, and why it will continue to be news for the coming years. Like the maps of the past or the Blue Marble photograph before it, it provides a powerful explanatory visual framework for an existential concern of our time: that another finiteness of our planet resides in the water resources we all share. GRACE has shown us that, indeed, we live on a fragile blue marble, one that, when it comes to the resources we directly depend on, is drying at an alarming rate.

The 23 October 2015 issue of the journal Science reported a feel-good story about how some children in India had received cataract surgery and were able to see. On the surface, there is nothing in this incident that should surprise us. Ready access to cataract surgery is something we take for granted. But the story is not that simple.

The children had been born with cataracts. They had never been able to see. By the time their condition was diagnosed — they came from impoverished and uneducated families in remote regions — the regional physicians had told the parents that it was too late because the children were past a critical period for gaining vision.

Nevertheless, a team of eye specialists visited the area and arranged for the cataract surgery to be performed even on teenagers. Now, hundreds of formerly blind children are able to see. After having the surgery four years earlier, one young man of 22 can ride a bicycle through a crowded market.

The concept of a critical period for developing vision was based on studies that David Hubel and Torsten Wiesel performed on cats and monkeys. The results showed that without visual signals during a critical period of development, vision is impaired for life. For humans, this critical window closes tight by the time a child is eight years old. (For ethical reasons, no comparable studies were run on humans.) Hubel and Wiesel won a Nobel Prize for their work. And physicians around the world stopped performing cataract surgery on children older than 8 years. The data were clear. But they were wrong. The results of the cataract surgeries on Indian teenagers disprove the critical period data.

In this light, an apparent “feel-good” story becomes a “feel-bad” story about innumerable other children who were denied the cataract surgery because they were too old. Consider all the children who endured a lifetime of blindness because of excessive faith in misleading data.

The theme of excessive faith in data was illustrated by another 2015 news item. Brian Nosek and a team of researchers set out to replicate 100 high profile psychology experiments that had been performed in 2008. They reported their findings in the 28 August 2015 issue of Science. Only about a third of the original findings were replicated and even for these, the effect size was much smaller than the initial report.

Other fields have run into the same problem. A few years ago the journal Nature reported a finding that the majority of cancer studies selected for review could not be replicated. In October 2015, Nature devoted a special issue to exploring various ideas for reducing the number of non-reproducible findings. Many others have taken up the issue of how to reduce the chances of unreliable data.

I think this is the wrong approach. It exemplifies the bedrock bias: a desire for a firm piece of evidence that can be used as a foundation for deriving inferences.

Scientists appreciate the tradeoff between Type I errors (detecting effects that aren’t actually present — false positives) and Type II errors (failing to detect an effect that is present — false negatives). When you put more energy into reducing Type I errors, you run the risk of increasing Type II errors, missing findings and discoveries. Thus we might change the required significance level from .05 to .01, or even .001, to reduce the chances of a false positive but in so doing we would greatly increase the false negatives.

The bedrock bias encourages us to make extreme efforts to eliminate false positives, but that approach would slow progress. A better perspective is to give up the quest for certainty and accept the possibility that any datum may be wrong. After all, skepticism is a mainstay of the scientific enterprise.

I recall a conversation with a decision researcher who insisted that we cannot trust our intuitions; instead, we should trust the data. I agreed that we should never trust intuitions (we should listen to our intuitions but evaluate them), but I didn’t agree that we should trust the data. There are too many examples, as described above, where the data can blind us.

What we need is the ability to draw on relevant data without committing ourselves to the validity of those data. We need to be able to derive inferences, make speculations, and form anticipations, in the face of ambiguity and uncertainty. And to do that, we will need to overcome the bedrock bias. We will need to free ourselves from the expectation that we can trust the data.

I am not arguing that it’s okay to get the research wrong — witness the consequence of all the Indian children who suffered unnecessary blindness. My argument is that we shouldn’t blind ourselves to the possibility that the data might be wrong. The team of Indian eye specialists responded to anecdotes about cases of recovered vision and explored the possible benefits of cataract surgery past the critical period.

The heuristics-and-biases community has done an impressive job of sensitizing us to the limits of our heuristics and intuitions. Perhaps we need a parallel effort to sensitize us to the limits of the data — a research agenda demonstrating the kinds of traps we fall into when we trust the data too much. This agenda might examine the underlying causes of the bedrock bias, and possible methods for de-biasing ourselves. A few cognitive scientists have performed experiments on the difficulty of working with ambiguous data but I think we need more: a larger, coordinated research program.

Such an enterprise would have implications beyond the scientific community. We live in an era of Big Data, an era in which quants are taking over Wall Street, an era of evidence-based strategies. In a world that is becoming increasingly data-centered, there may be value in learning how to work with imperfect data.

In 1974, Stephen Hawking proved that black holes were not black. Rather, quantum mechanics required that black holes would slowly lose their mass via a really neat mechanism: the gravity of a black hole would create a pair of particles outside the black hole, one particle with negative mass and the other with positive mass, and the former would fall inside the black hole, and the latter would move away from the black hole. The net effect was to decrease the mass of the black hole, and its mass would go to zero eventually.

Hawking realized that a zero-mass black hole was a big no-no, because such an entity could only be a naked singularity that completely destroyed the information inside the black hole. One of the fundamental principles of quantum mechanics—the same theory that tells us black holes evaporate—is “unitarity”, which means that information is conserved. But if a black hole destroys information in the final stages of its evaporation, then information cannot be conserved. Unitarity would be violated if a black hole were to evaporate completely.

Hawking then made a mistake: he argued that we have to accept a violation of unitarity in black hole evaporation. But unitarity is a really fundamental principle of quantum physics. Unitarity has many implications, one of which is that if unitarity is violated, so is the conservation of energy. And a little violation of unitarity is like being “just a little pregnant;” it has a tendency to get larger, very much larger. Leonard Susskind of Stanford University pointed out that a tiny violation of unitarity would give rise to a disastrous positive feedback of violation of energy: if one were to turn on a microwave oven, so much energy would be created out of nothing—conservation of energy does not hold, remember—that the Earth would be blown apart!

Obviously, this cannot happen. Information and hence energy must be conserved. But many black holes have been detected, so they must evaporate. How is this dilemma to be resolved?

There is an obvious resolution, namely that all observed black holes are the mass of the Sun or larger, and such black holes will last billions of trillions of years before they approach a naked singularity. What if the universe came to an end in a Big Crunch singularity before any black holes had time to evaporate completely?

This resolution of the black hole evaporation dilemma has a host of fascinating implications. First, it means that the Dark Energy, whatever it is, will eventually turn off. In an ever-accelerating universe, there will be no Big Crunch singularity, and all black holes will eventually evaporate.

Second, the great Israeli physicist Jacob Bekenstein—the man whose work suggested to Hawking that he should investigate the possibility of black hole evaporation—has proved mathematically that if event horizons exist, then the entropy of the universe must approach zero as a Big Crunch singularity is approached. But the Second Law of Thermodynamics says that entropy can never decrease, much less approach zero at the end of time. Thus, if the Second Law holds forever—which it does—then event horizons cannot exist. The absence of event horizons can be shown mathematically to imply that the universe must be spatially finite.

The absence of event horizons also incidentally, almost in passing, resolves the problem of how the information inside a black hole gets out: if there are no event horizons, there is literally no barrier to getting out. We should keep in mind that the assumption that a black hole is bounded by an event horizon is just that: an assumption, not an observed fact. If the universe ends in a Big Crunch, the information inside a black hole just would not get out until near the Big Crunch. No observation today can show that the information is forever bound to being inside the black hole. A claim that event horizons exist is like a guy’s claim that he is immortal: one would have to wait until the end of time to confirm the claim.

So by merely accepting the obvious resolution of Hawking’s Dilemma, and applying the standard laws of physics, we infer that the universe is spatially finite, that the Dark Energy will eventually turn off, that the universe will end in a Big Crunch, and that event horizons do not exist. Various physicists have pointed each of these facts over the past decade, but these implications seem to have escaped the science journalists. Eventually, the information will leak out. Hopefully before the end of time.

Deep learning neural networks are the most exciting recent technological and scientific development. Technologically, they are soundly beating competing approaches in a wide variety of contests including speech recognition, image recognition, image captioning, sentiment analysis, translation, drug discovery, and video game performance. This has led to massive investments by the big technology companies and the formation of more than 300 deep learning startups with more than $1.5 billion of investment.

Scientifically, these networks are shedding new light on the most important scientific question of our time: "How do we represent and manipulate meaning?" Many theories of meaning have been proposed that involve mapping phrases, sounds, or images into logical calculi with formal rules of manipulation. For example, Montague semantics tries to map natural language phrases into a typed lambda calculus.

The deep learning networks naturally map input words, sounds, or images into vectors of neural activity. These vector representations exhibit a curious "algebra of meaning." For example, after training on a large English language corpus, Mikolov's Word2Vec exhibits this strange relationship: "King - Man + Woman = Queen." His network tries to predict words from their surrounding context (or vice versa). The shift of context from "The king ate his lunch" to "The queen ate her lunch" is the same as from "The man ate his lunch" to "The woman ate her lunch." The statistics of many similar sentences lead to the vector from "king" to "queen" being the same as from "man" to "woman." It also maps "prince" to "princess," "hero" to "heroine," and many other similar pairs. Other "meaning equations" include "Paris - France + Italy = Rome," "Obama - USA + Russia = Putin," "Architect - Building + Software = Programmer." In this way, these systems discover important relational information purely from the statistics of training examples.

The success of these networks can be thought of as a triumph of "distributional semantics," first proposed in the 1950s. Meaning, relations, and valid inference all arise from the statistics of experiential contexts. Similar phenomena were found in the visual domain in Radford, Metz, and Chintala's deep networks for generating images. The vector representing a smiling woman minus the woman with a neutral expression plus a neutral man produces an image of the man smiling. A man with glasses minus the man without glasses plus a woman without glasses produces an image of the woman with glasses.

Deep learning neural networks are now being applied to hundreds of important applications. A classical challenge for industrial robots is to use vision to find and pick up a desired part from a bin of disorganized parts. An industrial robot company recently reported success at this task using a deep neural network with eight hours of training. A drone company recently described a deep neural network that autonomously flies drones in complex real-world environments. Why are these advances happening now? For these networks to learn effectively, they require large training sets, often with millions of examples. This, combined with the large size of the networks, means that they also require large amounts of computational power. These systems are having a big impact now because the web is a source of large training sets and modern computers with graphics coprocessors have the power to train them.

Where is this going? Expect these networks to soon be applied to every conceivable application. Several recent university courses on deep learning have posted their students' class projects. In just a few months, hundreds of students were able to use these technologies to solve a wide variety of problems that would have been regarded as major research programs a decade ago. We are in a kind of "Cambrian explosion" of these networks right now. Groups all over the world are experimenting with different sizes, structures, and training techniques and other groups are building hardware to make them more efficient.

All of this is very exciting but it also means that artificial intelligence is likely to soon have a much bigger impact on our society. We must work to ensure that these systems have a beneficial effect and to create social structures that help to integrate the new technologies. Many of the contest-winning networks are "feedforward" from input to output. These typically perform classification or evaluation of their inputs and don't invent or create anything. More recent networks are "recurrent nets" which can be trained by "reinforcement learning" to take actions to best achieve rewards. This kind of system is better able to discover surprising or unexpected ways of achieving an outcome. The next generation of network will create world models and do detailed reasoning to choose optimal actions. That class of system must be designed very carefully to avoid unexpected undesirable behaviors. We must very carefully choose the goals that we ask these systems to optimize. If we are able to develop the scientific understanding and social will to guide these developments in a beneficial direction, the future is very bright indeed!

This year, the New York Times, Wall Street Journal, Los Angeles Times, and other prominent news outlets around the world granted an abnormally high level of media coverage to scientific news about the world’s great ice sheets. The news conveyed was not good.

Through unprecedented new images, field measurements, and modeling capabilities, we now know that Greenland and Antarctica, remote as they are, have already begun the process of redefining the world’s coastlines. More than a billion people—and untold impacts to economies, ecosystems, and cultural legacies—will be altered, displaced or lost in the coming generations.

Five studies in particular commanded especial attention. One showed that the floating ice shelves ringing Antarctica (which do not affect sea level directly, but do prevent billions of tons of glacier ice from sliding off the continent into the ocean) are thinning, their bulwarking ability compromised. Another found pervasive blue meltwater rivers gushing across the ice surface of Greenland through the use of drones, satellites, and extreme field work. A major NASA program (called “Oceans Melting Glaciers” or OMG) showed that the world’s warming oceans—which thus far have absorbed most of the heat from rising global greenhouse gas emissions—are now melting the big ice sheets from below, at the undersides of marine-terminating glaciers. A fourth study used historical air photographs to map the scars of 20th century deglaciation around the edges of the Greenland ice sheet, showing that its pace of volume loss has accelerated. A fifth, very long time-horizon study used advanced computer modeling to posit that the massive Antarctic ice sheet may even disappear completely in coming millennia, should we choose to burn all known fossil fuel reserves.

That last scenario is extreme. But should we choose to bring it to reality, the world’s oceans would rise an additional 200 feet. To put 200 feet of sea level rise into perspective, the entire Atlantic seaboard, Florida, and Gulf Coast would vanish from the United States, and the hills of Los Angeles and San Francisco would become scattered islands. Even five or ten feet of sea level rise would change or imperil the existence of coastal populations as we currently know them. Included among these are major cities like New York, Newark, Miami, and New Orleans in the USA; Mumbai and Calcutta in India; Guangzhou, Guangdong, Shanghai, Shenzen, and Tianjin in China; Tokyo, Osaka, Kobe, and Nagoya in Japan; Alexandria in Egypt; Hai Phòng and Ho Chi Minh City in Vietnam; Bangkok in Thailand; Dhaka in Bangladesh; Abidjan in Côte d'Ivoire, Lagos in Nigeria, and Amsterdam and Rotterdam in The Netherlands. The risk is not simply of rising water levels, but also the enhanced reach of storm surges (as illustrated by the hurricane Katrina and superstorm Sandy); and of private capital and governments ceasing to provide insurance coverage for flood-vulnerable areas.

Viewed collectively, these studies and others like them, tell us four things that are interesting and important.

The first is that ice sheets are leaky, meaning it seems unlikely that increased surface melting from climate warming can be countered by significant retention or refreezing of water within the ice mass itself.

The second is that the pace of global sea level rise, which has already nearly doubled over the past two decades (and currently rising approximately 3.2 mm/year, on average), is clearly linked to the shrinking ice volumes of ice sheets.

The third is that warming oceans represent a hitherto unappreciated feedback to sliding ice.

The fourth is that the process of ice-triggered sea level rise is not only ongoing, but accelerating. Many glaciologists now fear that earlier estimates of projected sea level rise (about 1 foot if we act aggressively now to curb emissions, about 3.2 feet if we do not) by the end of this century.

Sea level rise is real, it’s happening now, and is here to stay. Only its final magnitude remains for us to decide.

In 2010, in England alone, it was estimated that mental illness cost over £100 billion to the economy. Around the same time, the cost in the US was estimated at $318 billion annually. It is important that we do what we can to reduce this burden. However, we have mostly been going about it the wrong way because the predominant models of mental illness do not work. They are mostly based on the assumption that there are discrete underlying causes, but this approach to mental illness reflects an essentialist bias that we readily apply when trying to understand complexity.

It seems trite to point out that humans are complex biological systems and that the way we operate requires sophisticated interactions at many different levels. It is even more remarkable then, that it has taken over 100 years of research and effort to finally recognize that when things break down, they involve multiple systems of failure and yet, until the last couple of years, many practitioners in the psychiatric industry of Western culture have been reluctant to abandon the notion that there are qualitatively distinct mental disorders that have core causal dysfunctions. Or at least that’s how the treatment regimes seem to have been applied.

Ever since Emil Kraepelin at the end of the 19th century advocated that mental illnesses could be categorized into distinct disorders with specific biological causes, research and treatment has focused efforts on building classification systems of symptoms as a way of mapping the terrain for discovering the root biological problem and corresponding course of action. This medical model approach led to development of clinical nosology and the accompanying diagnostic manuals such as the Diagnostic and Statistical Manual of Mental Disorder (DSM)—the most recent fifth version published in 2013. However, that very same year, the National Institute of Mental Health announced that it would no longer be funding research projects that relied solely on the DSM criteria. This is because the medical model lacks validity.

A recent analysis by Denny Borsboom in the Netherlands revealed that 50 percent of the symptoms of the DSM are correlated, indicating that comorbidity is the rule, not the exception, which explains why attempts to find biological markers for mental illness either through genetics or imaging have proved largely fruitless. It does not matter how much better we build our scanners, or refine our genetic profiling, as mental illness will not be reducible to Kraepelin’s vision. Rather, new approaches consider the way symptoms have causal effects between them rather than arising from an underlying primary latent variable.

Approaches to mental illness are changing. It is not clear what will happen to the DSM as there are vested financial interests in maintaining the medical model, but in Europe there is a notable shift towards symptom-based approaches of treatment. It is also not in our nature to consider the complexity of humans other than with essentialist biases. We do this for race, age, gender, political persuasion, intelligence, humor and just about every dimension we use to describe someone—as if these attributes are at the core of who they are.

The nature of the human mind is to categorize the world; to carve Nature up at its joints as it were, but in reality, experience is continuous. Moreover, the boundaries we create are more for our benefit than as a reflection of any true structures that exist. As complex biological systems, we evolved to navigate the complex world around us and thus developed the capacity to represent it in the most useful way as discrete categories. This is a fundamental feature of our nervous system from the input of raw sensory signals to the output of behavior and cognition. Forcing Nature into discrete categories optimizes the processing demands and the number of responses that need to be made, so it makes perfect sense from an engineering perspective.

Such insights are not particularly recent scientific discoveries and we should all be aware of them and yet, the essentialist perspective continues to shape the way that we go about building theories to investigate the world. Maybe it’s the best strategy when dealing with unknown terrain—assume patterns and discontinuities with broad strokes before refining your models to reflect complexity. The danger lies in assuming the frameworks you construct are real.

Recent headlines, such as those in the journal Nature, declared “Paradox at the heart of mathematics makes physics problem unanswerable,” and “Gödel’s incompleteness theorems are connected to unsolvable calculations in quantum physics.” Indeed, the degree to which mathematics describes, constrains, or makes predictions about reality is sure to be a fertile and important discussion topic for years or even centuries to come.

In 1931, mathematician Kurt Gödel determined that some statements are “undecidable,” suggesting that it is impossible to prove them either true or false. From another perspective, in his first incompleteness theorem, Gödel recognized that there will always be statements about the natural numbers that are true, but that are unprovable within the system. We now leap forward more than eighty years and learn that Gödel’s same principle appears to make it impossible to calculate an important property of a material, namely the gaps between the lowest energy levels of its electrons. Although this finding seems to concern an idealized model of the atoms in a material, some quantum-information theorists such as Toby Cubitt suggest that this finding limits the extent to which we can predict the behavior of certain real materials and particles.

Prior to this finding, mathematicians also discovered unlikely connections between prime numbers and quantum physics. For example, in 1972, physicist Freeman Dyson and number theorist Hugh Montgomery discovered that if we examine a strip of zeros from Riemann’s critical line in the zeta function, certain experimentally recorded energy levels in the nucleus of a large atom have a mysterious correspondence to the distribution of zeros, which, in turn, has a relationship to the distribution of prime numbers.

Of course there is a great debate as to whether mathematics is a reliable path to the truth about the universe and reality. Some suggest that mathematics is essentially a product of the human imagination, and we simply shape it to describe reality.

Nevertheless, mathematical theories have sometimes been used to predict phenomena that were not confirmed until years later. Maxwell’s Equations, for example, predicted radio waves. Einstein’s field equations suggested that gravity would bend light and that the universe is expanding. Physicist Paul Dirac once noted that the abstract mathematics we study now gives us a glimpse of physics in the future. In fact, his equations predicted the existence of antimatter, which was subsequently discovered. Similarly, mathematician Nikolai Lobachevsky said that “there is no branch of mathematics, however abstract, which may not someday be applied to the phenomena of the real world.”

Mathematics is often in the news, particularly as physicists and cosmologists make spectacular advances and even contemplate the universe as a wave-function and speculate on the existence of multiple universes. Because the questions that mathematics touches upon can be quite deep, we will continue to discuss the implications of the relationship between mathematics and reality perhaps for as long as humankind exists.

This year there has been an endless supply of news stories, as distinct from news itself, about Artificial Intelligence. Many of these stories concerned the opinions of eminent scientists and engineers who do not work in the field, about the almost immediate dangers of super intelligent systems waking up and not sharing human ethics, and being disastrous for mankind. Others have been from people within the field about the immorality of having AI systems make tactical military decisions. Still others have been from various car manufacturers about the imminence of self-driving cars on our roads. Yet others have been from philosophers (amateur and otherwise) about how such self driving cars will have to make life and death decisions.

My own opinions on these topics are counter to the popular narrative, and mostly I think everyone is getting way ahead of himself or herself. Arthur C. Clarke's third law was that any sufficiently advanced technology is indistinguishable from magic. All of these news stories, and the experts who are driving them, seem to me to be jumping so far ahead of the state of the art in Artificial Intelligence, that they talk about a magic future variety of it, and as soon as magic is involved any consequence one desires, or fears, can easily be derived.

There has also been a lot of legitimate news on Artificial Intelligence during 2015. Most of it centers around the stunning performance of deep learning algorithms, the back propagation ideas of the mid-1980's now extended by better mathematics to many more than just three network layers, and extended in computational resources by the massive computer clouds maintained by West Coast US tech titans, and also by the clever use of GPU's (Graphical Processing Units) within those clouds.

The most practical immediate effect of deep learning is that speech understanding systems are so noticeably better than just two or three years ago, enabling new services on the web or on our smart phones and home devices. We can easily talk to them now and have them understand us. The frustrations of using speech interfaces of five years ago are completely gone.

The success of deep learning has, I believe, led many people towards wrong conclusions. When a person displays a particular performance in some task, translating text from a foreign language, say, we have an intuitive understanding of how to generalize to what sort of competence the person has. For instance, we know that the person understands that language, and could answer questions about which of the people in a story about a child dying in a terrorist attack, say, were sad, which will mourn for months, and which felt they had achieved their goals. But the translation program likely has no such depth of understanding. One cannot apply the normal generalization from performance to competence that works for people to make similar generalizations for Artificial Intelligence programs.

Towards the end of the year we have started to see a trickle of news stories that are running counter to the narrative of runaway success of Artificial Intelligence. I welcome these stories as they strike me as bringing some reality back to the debates about our future relationship to AI. And there are two sorts of stories we have started to see.

The first class of stories is about the science, where many researchers are now vocally pointing out that there is a lot more science to be done in order to come up with learning algorithms that mimic the broad capabilities of humans and animals. Deep learning by itself will not solve many of the learning problems that are necessary for general Artificial Intelligence, for instance where spatial or deductive reasoning is involved. Further, all the breakthrough results we have seen in AI have been years in the making, and there is no scientific reason to expect there to be a sudden and sustained series of them, despite the enthusiasm from young researchers who were not around of the last three waves of such predictions in the 1950's, 1960's, and 1980's.

The second class of stories is about how self-driving cars and drivers of other cars interact. When large physical kinetic masses are in close proximity to human beings, the rate of adoption has been much slower than that, say, of Java Script in web browsers. There has been a naive enthusiasm that fully self-driving cars will soon be deployed on public roads. The reality is that there will be fatal accidents (even things built by incredibly smart people, sometimes blow up) that will cause irrational levels of caution when compared to the daily death toll world wide of more than 3,000 automobile fatalities caused by people. But the most recent news stories are documenting the high accident rate of self-driving cars under test. So far, all are minor accidents, and all are attributable to errors on the part of the other driver, the human. The cars are driving perfectly, goes the narrative, and not breaking the law like all humans do, so it is the humans that are at fault. When you are arguing that those pesky humans just don't get a technology, you have already lost the argument. There is a lot more work to be done before self-driving cars will ever be let loose in environments where ordinary people are also driving, no matter how shiny the technology seems to the engineers and VCs who are building it.

The over-hype in the news of AI from 2014 and 2015 is finally getting met with a little pushback. There will be a lot of screams of indignation from true believers, but eventually this bubble will fade into the past. At the same time we will gradually see more and more effective uses of AI in all our lives, but it will be slow and steady, and not explosive, and not existentially dangerous.

One morning, 3 years ago, my talented PhD student Dwaipayan Adhya (known affectionately in the lab as Deep) came into my office. He looked at me straight in the eye, and said he’d like to grow autistic and typical neurons (nerve cells) in a dish, from the earliest moment of development, to observe how the autistic neuron differs from the typical neuron, day by day. I dropped everything and listened.

Sounds like science fiction? You might imagine that to grow a brain cell in a dish the scientist would first have to pluck a neuron from a human embryo, keep it alive in a lab petri dish, and then watch it under the microscope, measuring how it grows day by day. If that’s what you’re imagining, you’re wrong. There is no way to get a neuron from a human embryo in any ethically acceptable way, for obvious reasons.

So what method was Deep planning to use, if there are no ways to study the development of the embryonic human brain in a prospective (forward) direction?

Deep told me about Shinya Yamanaka in Kyoto, who won the Nobel Prize in 2012 (along with Cambridge scientist John Gurdon) for his work on induced pluripotent stem cells or iPSC. In the lab, we call this magic. Here’s how it works.

Pluck a hair from the head of an adult, then take the follicle from that hair, and using the Yamanaka method, reverse the cell, backwards from the adult hair follicle, back into the state of a stem cell; that is, back to being an undifferentiated cell, before it specialized into becoming a hair follicle. This is not an embryonic stem cell – it is an “induced pluripotent” stem cell. Induced, because the scientist has forced the adult hair follicle (though you could use any cell in the body), by genetic reprogramming, to go back into the state of a stem cell. And pluripotent, meaning it can now be genetically programmed to become any kind of cell in the body that the scientist chooses: an eye cell, a heart cell, or a neuron. If the latter, this is referred to as “neuralizing” the iPSC.

Now you can see why we call this magic. I said to Deep, let’s do it! It seems entirely ethical, as most adults would be happy enough to donate a single hair from their head, and no animal is “sacrificed” in this kind of science, and it enables scientists to study the development of human neurons in the lab.

The importance of Yamanaka’s scientific breakthrough is that if you want to study development from the first moment of life, iPSC bypasses the need for an embryo. In addition, previously, if you wanted to understand the autistic brain, scientists would rely on post-mortem studies, where someone with autism has tragically died, and where their next of kin donate their relative’s brain to scientific research.

Brain donations are invaluable, but from a scientific perspective, post-mortem brain tissue has many limitations. For example, you may end up with a set of brains that are donated from individuals of different ages, each of whom died from different causes. Interpretation of results thus becomes difficult. A further complication is that you may know very little about the person before they died (e.g., what their IQ or personality was like) and it is too late to gather such information. Post-mortem studies are still informative, but come with a handful of caveats.

Alternatively, if you want to study the autistic brain, you can use an animal model, where for example you create a “knock out” mouse—a mouse genetically engineered to lack a particular gene that you as a scientist suspect may play a role in autism—and observe the behavior of the knock-out mouse compared to a typical (or wild-type) mouse. If the knock out mouse shows “autistic” behavior, for example being less sociable, you conclude this gene may be causing one or other of the symptoms of human autism. You can see the limitations of such animal studies immediately: how do you know that sociability in a mouse is the same thing as sociability in a human? The interpretation of results from such animal experiments is as littered with caveats as are the post-mortem studies.

Now we can see the power of adding iPSC to the scientist’s tool kit for getting answers to questions. If you want to observe the living human brain, you can study the brain from the person you are interested in, and you can gather as much information about that person as you want: IQ, personality, precise diagnosis, or anything else you want. You can even look at the effects of different drugs or molecules on the neuron, without having to do these arguably unethical drug studies on an animal.

iPSC is not without its own limitations. An iPSC may not be exactly identical to an embryonic stem cell, so the neuralized iPSC may not be exactly the same as a naturally growing neuron. All tools in the scientist’s tool kit have their limitations, but this one—to my mind—is more ethical, and a more directly relevant method to autism, than is animal research. Many labs (like ours) are testing if you get the same results from both iPSC and post-mortem studies, since this strengthens the conclusions that can be drawn.

Deep’s exciting results will be published in 2016. The combination of a break-through scientific method, in the hands of a talented young PhD student, might be just the cocktail to be a game-changer in our understanding of the causes of autism.

As an adolescent in the fifties, along with many others, I dreamt of the Space Age. We knew what the Space Age was supposed to look like: silver, bullet-shaped rockets rising into the sky on a column of flame, and as they return, descending on an identical column of flame, landing gently at the spaceport. Those dreams led me to a degree in astronautical engineering at RPI.

The reality of space flight turned out to very different. We built multistage booster rockets that were thrown away after every launch. Bringing them back turned out to be too hard. Carrying enough fuel to power the landing and managing the very turbulent flow of the rocket exhaust as the vehicle slowly descends on that violent roaring column of flaming gas was too great a challenge. Indeed even the efforts to build vertical take off and landing jet fighters in the fifties also failed for similar reasons.

The disposable launch vehicle made the Space Age too costly for most applications. Getting any mass into orbit cost many thousands of dollars per pound. Imagine what an airline ticket would cost if the airline threw away the aircraft after every flight. The booster vehicle generally costs around a few hundred million dollars, about the cost of a modern jet liner and we only get one use out of it. No other country, like Russia or China, or any company, like Boeing or Lockheed—all of which have huge resources—were able to solve the technical problems of a reusable booster.

The space shuttle was intended to meet this challenge by being reusable. Unfortunately, the cost of refurbishing it after each launch was so great that the cost of a shuttle launch was far greater than a disposable launcher flight. When I worked on mission planning for the space shuttle at SRI in the early seventies the assumption was that the cost of each launch would a be $118 per pound ($657 in current dollars), justifying many applications with each shuttle flying once a month. Instead, the shuttles could only fly a couple of times per year at a cost of $27,000 per pound, meaning most applications were off the table. So space was inaccessible except for those whose needs justified the huge costs either of a shuttle or a single-use booster: the military, telecommunications companies, and some government-funded, very high cost science.

But in the last few weeks of 2015 all that changed as the teams from two different startup rocketry companies, Blue Origin and Space X, made breakthroughs in bringing their launchers back to a vertical landing at the launch site. Both of their rockets were able to control that torrent of flaming gas to bring them to a gentle landing, ready to be prepared for another launch. Provided that they can do this on a regular basis, the economics of space flight have suddenly and fundamentally changed, coming to resemble airline flight with reusable boosters instead of reusable aircraft. It won’t be cheap yet, but many more applications will be possible. And the costs will continue to fall with experience.

While they both solved the very hard problem of controlling the vehicle at slow speed on a column of turbulent gas, the Space X achievement will be more consequential in the near future. The Blue Origin rocket could only fly to an altitude of 60 miles before returning to Earth and is intended mainly for tourism. The Space X vehicle, Falcon 9, could and did launch a second stage that can achieve Earth orbit. And Space X already ferries supplies and may soon be carrying astronauts to the Space Station orbiting the Earth. So the ability to reuse their most expensive component will reduce their launch costs by as much as 90 percent and over time those costs will continue to decline. Boeing and Lockheed should be worried.

Of course, the Blue Origin rocket, New Shepard, will also continue to improve. Their real competition is with Virgin Galactic, which had some difficulties lately—a crash that killed one of the pilots. They are both competing for the space tourism market and (for now) Blue Origin appears to be ahead.

We have turned a corner in space flight. We can dream of a Space Age again. Life in orbit becomes imaginable. Capturing asteroids to mine or human interplanetary exploration both become much more likely. The idea that many of us living today will be able to see the Earth from space is no longer a distant dream.

We are still rolling down the track created by Moore’s law, which means that news about science and technology will continue to focus on computers getting smaller, smarter, faster, and increasingly integrated into the fabric of our everyday lives—in fact, integrated into our bodies as prosthetic organs and eyes. Our cyborg selves are being created out of advances not only in computers but also in computer peripherals. This is the technology that allows computers to hear, touch, and see.

Computers are becoming better at “seeing” because of advances in optics and lenses. Manufactured lenses, in some ways better than human lenses, are getting cheap enough to put everywhere. This is why the news is filled with stories about self-driving cars, drones, and other technology that relies on having lots of cameras integrated into objects.

This is also why we live in the age of selfies and surveillance. We turn lenses on ourselves as readily as the world turns lenses on us. If once we had a private self, discrete from posing, this self has disappeared into curated images of ourselves doing stuff that provokes envy in the hearts of our less-successful “friends.” If once we walked down streets with our gaze turned outward on the world, now we walk with our eyes focused on the screens that mediate this world. At the same time, we are tracked by cameras that record our motion through public space which has become monitored space.

Lenses molded from polymers cost pennies to manufacture, and the software required to analyze images is getting increasingly smart and ubiquitous. Lenses advanced enough for microscopy now cost less than a dollar. The latest issue of Nature Photonics, reporting on work done by researchers in Edinburgh, describes cameras that use photons for taking pictures around corners and in other places that the human eye can’t see. This is why our self-driving cars will soon have lower insurance rates than the vehicles that we currently navigate around town.

The language of sight is the language of life. We get the big picture. We focus on a problem. We see—or fail to see—each other’s point of view. We have many ways of looking, and more are being created every day. With computers getting better at seeing, we need to keep pace with understanding what we’re looking at.

The education field today is much like medicine was in the 19th century—a human practice, guided by intuition, experience, and occasionally inspiration. It took the development of modern biology and biochemistry in the early part of the 20th century to provide the solid underpinnings of today’s science of medicine.

To me—a mathematician who became interested in mathematics education in the second half of my career—it seems that we may at last be seeing the emergence of a genuine science of learning. Given the huge significance of education in human society, that would make it one of the most interesting and important of today’s science stories.

At the risk of raising the ire of many researchers, I should note that I am not basing my assessment on the rapid growth in educational neuroscience. You know, the kind of study where a subject is slid into an fMRI machine and asked to solve math puzzles. Those studies are valuable, but at the present stage, at best they provide at most tentative clues about how people learn, and little specific in terms of how to help people learn. (A good analogy would be trying to diagnose an engine fault in a car by moving a thermometer over the hood.) One day, educational neuroscience may provide a solid basis for education the way, say, the modern theory of genetics advanced medical practice. But not yet.

Rather, I think the emergence of a science of learning arises from the possibilities Internet technology brings to the familiar, experimental cognitive science approach.

The problem that has traditionally beset learning research has been its essential dependence on the individual teacher, which makes it near impossible to run the kinds of large scale, control group, intervention studies that are par-for-the-course in medicine. Classroom studies invariably end up as studies of the teacher as much as of the students, and often measure the effect of the students’ home environment rather than what goes on in the classroom.

For instance, news articles often cite the large number of successful people who as children attended a Montessori school, a figure hugely disproportionate to the relatively small number of such schools. Now, it may well be the case that the Montessori educational principles are good, but it’s also true that such schools are magnets for passionate, dedicated teachers and the pupils that attend them do so because they have parents who go out of their way to enroll their offspring in such a school, and already raise their children in a learning-rich home environment.

Internet technology offers an opportunity to carry out medical-research-like, large scale control group studies of classroom learning that can significantly mitigate the “teacher effect” and “home effect,” allowing useful studies of different educational techniques to be carried out. Provided you collect the right data, Big Data techniques can detect patterns that cut across the wide range of teacher-teacher and family-family variation, allowing useful educational conclusions to be drawn.

An important factor is that a sufficiently significant part of the actual learning is done in a digital environment, where every action can be captured. This is not easily achieved. The vast majority of educational software products operate around the edges of learning: providing the learner with information; asking questions and capturing their answers (in a machine-actionable, multiple-choice format); and handling course logistics with a learning management system.

What is missing is any insight into what is actually going on in the student’s mind—something that can be very different from what the evidence shows, as was dramatically illustrated for mathematics learning several decades ago by a study now famously referred to as “Benny’s Rules,” where a child who had aced a whole progressive battery of programmed learning cycles was found (by a lengthy, human–human working session) to have constructed an elaborate internal, rule-based “mathematics” that enabled him to pass all the tests with flying honors, but which was completely false, and bore no relation to actual mathematics.

But real-time, interactive software allows for much more than we have seen flooding out of tech hotbeds such as Silicon Valley. To date, some of the more effective uses from the viewpoint of running large-scale, comparative learning studies, have been by way of learning video games—so-called game-based learning. (It remains an open question how significant is the game element in terms of learning outcomes.)

In the case of elementary through middle school mathematics learning (the research I am familiar with), what has been discovered, by a number of teams, is that digital learning interventions of as little as ten minutes a day, over a course of as little as one month, can result in significant learning gains when measured by a standardized test—with improvements of as much as 20 percent in some key thinking skills.

That may sound like an educational magic pill. It almost certainly is not. It’s most likely an early sign that we know even less about learning than we thought we did.

For one thing, part of what is going on is that many earlier studies measured knowledge rather than thinking ability. The learning gains found in the studies I am referring to are not knowledge acquired or algorithmic procedures mastered, rather high-level problem solving ability.

What is exciting about these findings, is that in today’s information—and computation—rich environment, those very human problem-solving skills are the ones now at a premium.

Like any good science, and in particular any new science, this work has generated far more research questions than it has answered.

Indeed, it is too early to say it has answered any questions. Rather, as of now we have a scientifically sound method to conduct experiments at scale, some very suggestive early results, and a resulting long and growing list of research questions—all testable. Looks to me like we are about to see the final emergence of a genuine science of learning.

The biggest news of 2015 was recognition that new abilities to edit specific genes will transform life itself, transform it utterly. Simple enough to be implemented in labs everywhere, the CRISPR-Cas9 technique replaces a specified DNA sequence with a chosen alternative. The system has revolutionized genetic research, and it offers hope to those with genetic diseases. It also makes it easy, however, to change future generations. It even lends itself to creating “gene-drives” that can, in just a few generations, replace a given sequence in all members of a sexually reproducing species. A terrible beauty has been born.

The possibilities are beyond our imagining, but some are already real. Trials in caged mosquitos demonstrate fast transmission of a new gene providing resistance to malaria. If released into the wild, the gene could spread and eliminate malaria. Would it? What else would it do? No one knows. Other gene drives could entirely eliminate a species. Good riddance to smallpox, but what would happen to ecosystems without mice and mosquitoes? No one knows.

Specters from the Disney cartoon The Sorcerer’s Apprentice come to mind. How will a species with limited ability to control itself use such vast new power? The answer will determine the future of our species and life on this planet. In a remarkable demonstration of transparency and foresight, the National Academy of Sciences organized a meeting earlier this month with sibling organizations from the UK and China to discuss the opportunities and threats. The risks were taken seriously, but there was little consensus on where this technology will take us, and how or if we can control it. It seems likely that it will transform our species and life itself, fast. How, no one knows.

If you find yourself at a cocktail party searching for a conversation starter, I’d recommend working in the opening line of a recent Bill and Melinda Gates Annual Letter: "By almost any measure, the world is better than it has ever been.” Although people will react with incredulity at the very possibility that things could be getting better, they’ll welcome the opportunity to straighten you out. Just be prepared for the inevitable recitation of the daily headlines—bad news piled on top of even worse news—that will inevitably follow. Virtually everyone I’ve mentioned this quote to is sure it¹s wrong.

For example, about two-thirds of Americans believe the number of people living in extreme poverty has doubled in the last 20 years. People point to conflicts in the Middle East as evidence of a world in chaos, the retreat of democracy, plummeting human rights, and an overall global decline of wellbeing. Yet the news from social science through the accumulation of large-scale, longitudinal datasets belies this declinist worldview.

This isn¹t the place to delve into the details of how large-scale statistical datasets, and ones increasingly representative of the world¹s population, provide a more accurate, though deeply counter-intuitive, assessment of the state of the world (for that, see Steven Pinker’s The Better Angels of our Nature).

In reality, extreme poverty has nearly halved in the last twenty years—about a billion people have escaped it. Material wellbeing—income, declines in infant mortality, increases in life expectancy, educational access (particularly for females)—has increased at its greatest pace during the last few decades. The number of democracies in developing nations has tripled since the 1980s, while the number of people killed in armed conflicts has decreased by 75%. This isn¹t the place to delve into the details of how large-scale statistical datasets, and ones increasingly representative of the world’s population, provide a more accurate, though deeply counter-intuitive, assessment of the state of the world.

Instead, I want to suggest three reasons why I think it’s such important scientific news. First, while these long-term trends may not resuscitate an old-fashioned notion of progress—certainly not one suggesting that history possesses intrinsic directionality—they do call out for a better understanding (and recognition) of the technological and cultural dynamics driving long-term patterns of historical change. What is even more intriguing to me is their stark demonstration of how deeply our cognitive and emotional biases distort our worldview. In particular, we have good evidence that we don’t remember the past as it was. Instead, we systematically edit it, typically omitting the bad and highlighting the good, leading to cognitive biases, such as “rosy retrospection.”

At the cultural level, these biases make us biologically vulnerable to declinist narratives. From Pope Francis’ anti-modernist encyclical to Capitalism¹s inevitable death by internal contradictions and tales of moral decline, declinist narratives intuitively resonate with our cognitive biases. They thus make for an easy sell and make it easy to lose sight of the fact that until a few centuries ago the world¹s population was stuck in abject poverty, a subsistence-level Malthusian trap of dreary cycles of population growth and famine.

In reality, not only has material wellbeing increased around the globe, global inequality is also decreasing as a result of technological and cultural innovations driving globalization. We should be particularly on guard against declinist narratives that also trigger our emotional biases, especially those hijacking the brain’s low-level treat detection circuits. These alarmist narratives identify an immediate or imminent threat, a harbinger of decline, which unconsciously triggers the amygdala and initiates a cascade of brain chemicals, norepinephrine, acetylcholine, dopamine, serotonin, and hormones, adrenalin and cortisol, creating both primal visceral feelings of dread and locking in our attention to that narrative, effectively shutting down rational appraisal.

Much of what counts as “news” today involves such narratives. The combination of an ever-shortening news cycle, near instantaneous communications, fragmented markets, heightened competition for viewership, and our cognitive and emotional biases conspire to make it all but inevitable that these narratives would dominate and make it prohibitive to grasp the progressive themes large-scale data analyses reveal.

The result is today’s dominant alarmist and declinist news cycle that’s essentially a random walk from moral panic to moral panic. To appreciate the real news—that by many fundamental measures the state of the world is improving—thus requires an exercise in cognitive control, inhibiting our first emotional impulses and allowing a rational appraisal of scientifically informed data. This by no means constitutes some Pollyannaish exercise of denial. But the most important scientific news to me is that the broad historical trajectory of human societies provides a powerful counter-narrative to today’s dominant declinist worldview.

The news stories from China are horrific. The best estimate is that on average, 4,400 people die every day from air pollution in that country. That’s 1.6 million per year. Every time I hear of some tragedy that makes headlines, such as a landslide in Shenzhen that killed 200 people, I think to myself, “Yes — and today 4,400 people died of air pollution and it didn’t make the news.”

This is not the old eye-burning throat irritating air pollution of yesterday. Today’s pollutant is known as PM2.5, particulate matter 2.5 microns and smaller. It is produced by automobiles, by construction, by farm work, but the greatest contributor by far is coal, burned by industry and for electric power production. PM2.5 wasn’t even listed as a major pollutant by the US EPA until 1997. It was present, but jut not fully proven to be as deadly as it is.

We now know that on a bad day in Beijing, such pollution hurts people as much as smoking two packs of cigarettes each day — for every man woman and child who breathes it. Bad air triggers strokes, heart attacks, asthma, and lung cancer. Look at the causes of death in China and you’ll see a remarkable excess of such deaths, despite the fact that obesity is small compared to that in the US.

We know about the health effects from some remarkable studies. In the US we saw decreases in health problems when factories and coal plants were temporarily shut down; this is the famous “Six Cities Study.” In China, we have the Huai River Study, in which the Chinese policy of giving free coal to households north of the Huai River, but none to the south, resulted in a reduced average lifetime in the north of 5.5 years.

Also remarkable is China’s openness with their air pollution data. Every hour they post online over 1500 measurements of PM2.5 (as well PM10, SO2, NO2, and ozone) all across their country. China may be a closed country in many ways, but they seem to be crying out seeking help. At Berkeley Earth we have been downloading all these numbers for the past year and a half, and the patterns of severe pollution are now clear. It is not confined to cities or basins, but widespread and virtually inescapable. 97% of China’s population breathes what our EPA deems as “unhealthy air “on average.

In contrast, the democracy of India reports few PM2.5 measurements. I suspect they have them but are simply not making them public. They do publish results for Delhi, and virtually every time I look, the pollution there is worse than it is in Beijing.

People suggest a switch from coal to solar, but it is too expensive for China to afford. In 2015, solar power contributed less than 0.2% to their energy use, and solar plants are going bankrupt as the Chinese subsidies are withdrawn. Wind is expanding, but its intermittency is a big problem, and the use of energy storage drives up cost. Hydro is hardly an environmental choice; the 3-Gorges Dam displaced 1.2 million people (voluntarily, the Chinese tell us) and destroyed 13 cities, 140 towns, and 1350 villages. Their new Mekong River dam is expected to wreak havoc throughout Myanmar, Thailand and Viet Nam.

The best hopes consist of natural gas, which China has in abundance, and nuclear power, which is under rapid development. PM2.5 from natural gas is reduced by 1/400 compared to coal — and it reduces greenhouse emissions by a factor of 2 to 3. China is desperately attempting to extract its shale gas, but is doing miserably; the only true master of that technology is the US, where it has triggered an enormous and unexpected drop in the price of both natural as and oil. Nuclear, once despised by the environmentalists, is gaining traction in the US, with many past opponents recognizing that even in the US it offers a way to reduce carbon emissions significantly. China is surging ahead in nuclear, with 32 new plants planned. Although such plants have a reputation of being expensive, the Chinese know that the high cost is only in the capital cost; that amortized over 25 years, nuclear is as cheap as coal, and much cheaper when you add in the environmental costs.

Air pollution is going to be a growing story in the future. China also has plans, on paper, to double its coal use in the next 15 years. They will cancel those if they can, but they also worry that slower economic growth could threaten their form of government. As bad as the pollution has been so far, I worry that we ain’t seen nothing yet.

The United States is sharing its nuclear technology, and I expect that in two decades China will be the principle manufacturer of nuclear power plants around the world. But we need to set a better example; we need to show the world that we consider nuclear to be safe. And we need to share our shale gas technology far more extensively. Too often we read the pollution headlines, shake our heads, perhaps feel a little schadenfreude towards our greatest economic adversary, and then we forget about it.

Some day global warming may become the primarily threat. But it is air pollution that is killing people now. Air pollution is the greatest environmental disaster in the world today.

I’m most interested by the news that an increasing number of people are rejecting science, altogether. With 31% of Americans believing that human beings have existed in their current form since the beginning, and only 35% percent agreeing that evolution happened through natural processes, it’s no wonder that parents reject immunization for their children and voters support candidates who value fervor over fact.

To be sure, science has brought some of this on itself, by refusing to admit the possibility of any essence to existence, and by too often aligning with corporate efforts to profit off discoveries with little concern for their long-term impact on human well-being.

But the dangers of an anti-scientific perspective, held so widely, are particularly perilous at this moment in technological history. We are fast acquiring the tools of creation formerly relegated to deities. From digital and genetic programming to robots and nanotechnology, we are developing things that—once created—will continue to act on their own. They will adapt, defend, and replicate, much as life itself. We have evolved into the closest things to gods this world has ever known, yet a majority of us have yet to acknowledge the actual processes that got us to this point.

That so many trade scientific reality for provably false fantasy at precisely the moment when we have gained such powers may not be entirely coincidental. But if these abilities are seized upon as something other than the fruits of science, and if they are applied with utter disregard to the scientific context through which they were developed, I fear we will lack the humility required to employ them responsibly.

The big science story of the century—one that may even decide our fate—will be whether or not we accept science at all.

There are, by some accounts, more than 10,000 religious traditions in the world. Every day, somewhere in the world, a new religious movement is in the making.

But this theodiversity—a term I borrow from Toby Lester—is not evenly distributed in human populations anymore than biodiversity is evenly distributed on the planet. The reasons for this aren’t very well understood, but the overwhelming majority of religious movements throughout history are failed social experiments. Most never take hold, and of those that do, don’t last for very long—not even for a few decades. Of those that last for a while, most stay small.

Then there are the “world religions.” Christianity, Hinduism, and Islam especially, have been growing at a brisk pace. Buddhism is much smaller and not growing much, but still a sizable presence on the world stage.

We are at a point in time when just a few religious traditions have gone global, making up the vast majority of believers in the world.

This fact is detailed in a landmark Pew Research Center report, released on April 2, 2015. It’s the most comprehensive and empirically derived set of projections based on data, age, fertility, mortality, migration, and religious conversion/de-conversion for multiple religious groups around the world. Barring unforeseen shocks, if current demographic and social trends keep up, by 2050:

Possibly for the first time in history, there will be as many Muslims as Christians in the world. Together, these two faiths will represent more than 60 percent of the world’s projected population of 9.5 billion.

40 percent of Christians in the world will live in sub-Saharan Africa (the region that will have the largest share of Christians), compared to 15 percent living in Europe. This means that the epicenter of Christianity will finally shift from Europe to Africa.

India, while maintaining a Hindu majority, will have the largest Muslim population in the world, surpassing Indonesia and Pakistan.

All the folk religions of the world combined will comprise less than 5 percent of the world’s population.

1.3 billion people, or 13.5 percent of the world’s population in 2050, will be non-religious.

One might think that religious denominations that have adapted to secular modernity the best are the ones that are thriving the most. But the evidence gleaned from the Pew report and other studies points in the exactly opposing direction. Moderate denominations are falling behind in the cultural marketplace. They are the losers caught between secular modernity and the fundamentalist strains of all major world religions, which are gaining steam as a result of conversion, higher fertility rates, or both.

There are different types, shades, and intensities of disbelief. That’s why the non-religious are another big ingredient of the world’s astonishing and dynamically changing theodiversity. Combined, they would be the fourth largest “world religion.” There are the atheists, but many nonbelievers instead are apatheists, who are indifferent towards but not opposed to religions. And there is the rising demographic tide of people who see themselves as “spiritual but not religious.” This do-it-yourself, custom-made spirituality is filling the void that the retreat of organized religion is leaving behind in the secularizing countries. You can find it in yoga studios, meditation centers, the holistic health movement, and eco-spirituality.

Theodiversity once was the exclusive subject matter of the humanities. But it is now a focal point of a budding science-humanities collaboration. The religious diversification of humankind in historical time poses fascinating questions and challenges for the new science of cultural evolution. Also, these are times of renewed anxieties about real or imagined cultural conflict between religions, and between religions and secular modernity. This is why quantifiable, evidence-based, and nuanced understanding of the complexities of theodiversity are important now more than ever.

I'll try to report the news from the physics front that I think may prove to be important. When I say important I mean to someone interested in how the physical world operates.

First of all from the experimental front there is news from the LHC—the big particle collider in Europe. There is evidence of a new particle. What a new particle means at this stage is a small bump in a data distribution. It could be real or it could be a statistical fluke, but if it is real it does represent something new. Unlike the Higgs particle it is not part of the standard model of particle physics. In fact, to my knowledge the new particle does not fit neatly into any theoretical framework, such as supersymmetry or technicolor, and its not a black hole or a graviton. So far it just seem to be an extra particle.

If it's real, not just a fluke, then there will probably be more particles uncovered, and not only new particles but new forces. Perhaps a whole new structure on top of the standard model. I don't think that at the moment anyone has a compelling idea of what it means. It is possible that it is connected to the puzzle of dark matter, i.e., the missing matter in the universe that seeded the galaxies. The new particle is not itself dark matter—it's too short lived—but other related particles could be.

From the more theoretical side the thing I find most interesting is new ideas that relate gravity, the structure of space, and quantum mechanics. For example there is gathering evidence (all theoretical) that quantum entanglement is the glue that holds space together. Without quantum entanglement space would fall apart into an amorphous unstructured unrecognizable thing.

Another idea (full disclosure: it is my idea) is that the emergence of space behind the horizons of black holes is due to the growth of quantum complexity. This is too technical to explain here except to say that it is a surprising new connection between physics and quantum-information science. It's not completely far fetched that these connections may not only teach us new things about fundamental physics problems, but also be tools for understanding the more practical issues of construction and using quantum computers. Stranger things have happened.

For 2500 years moral philosophy was entrusted to philosophers and theologians. But in recent years moral philosophers who are also cognitive scientists and cognitive scientists with a sophisticated mastery of moral philosophy have transformed moral philosophy. Findings and theories from many branches of cognitive science have been used to reformulate traditional questions and to defend substantive views on some of the most important moral issues that face contemporary societies. In this new synthesis, the cognitive sciences are not replacing moral philosophy. Rather, they are providing new insights into the psychological and neurological mechanisms underlying moral reasoning and moral judgment, and these insights are being used to construct empirically informed moral theories that are reshaping moral philosophy.

Here’s the backstory: From Plato onward, philosophers who were concerned with morality have made claims about the way the mind works when we consider moral issues. But these claims were always speculative and often set out in metaphors or allegories. With the emergence of scientific psychology, in the 20th century, psychologists became increasingly interested in moral judgment and moral development. But much of this work was done by researchers who had little or no acquaintance with the rich philosophical tradition that had drawn important distinction and defended sophisticated positions about a wide range of moral issues. So philosophers who dipped into this work typically found it naïve and unhelpful.

At the beginning of the current century that began to change. Prompted by the interdisciplinary zeitgeist, young philosophers (and a few who weren’t so young) resolved to master the methods of contemporary psychology and neuroscience and use them to explore questions about the mind that philosophers had been debating for centuries. On the other side of the disciplinary divide, psychologists, neuroscientists and researchers interested in the evolution of the mind began to engage with the philosophical tradition more seriously. What began as a trickle of papers that were both scientifically and philosophically sophisticated has turned into a flood. Hundreds of papers are published every year, and moral psychology has become a hot topic. There are many examples of this extraordinary work. I’ll mention just three.

Joshua Greene is in many ways the poster child for the new synthesis of cognitive science and moral philosophy. While working on his PhD in philosophy, Greene had the altogether novel idea of asking people to make judgments about moral dilemmas while in a brain scanner.

Philosophers had already constructed a number of hypothetical moral dilemmas in which a protagonist was required to make a choice between two courses of action. One choice would result in the death of five innocent people; the other would result in the death of one innocent person. But philosophers were puzzled by the fact that in very similar cases people sometimes chose the to save five and sometimes chose to let the five die.

What Greene found was that different brain regions were involved in these choices. When the five were saved the brain regions involved were thought to be associated with rational deliberation; when the five were not saved, the brain regions involved were thought to be associated with emotion.

This early result prompted Greene to retrain as a cognitive neuroscientist and triggered a tsunami of studies exploring what is going on in the brain when people make moral judgments. But though Greene became a cognitive scientist, he was still a philosopher, and he draws on a decade of work in moral psychology to defend his account of how moral decisions that divide groups should be made. Greene illustrates the way in which moral philosophy can be transformed by cognitive science.

If Greene is the poster child for the new synthesis, John Mikhail is its renaissance man. While completing a philosophy PhD, he spent several years studying cognitive science and then got a law degree. He’s now law professor where his areas of expertise include human rights law and international law.

Drawing on the same family of moral dilemmas that were center stage in Greene’s early work, Mikhail has conducted an extensive series of experiments that, he argues, support the view that all normal humans share an important set of innate moral principles. Mikhail argues that this empirical work provides the much needed intellectual underpinning for the doctrine of universal human rights!

And finally an example—one among many—of the new questions that the new synthesis has enabled us to see. Recent work in psychology has revealed that we all have a grab bag of surprising implicit biases. Many people, including people who support and work hard to achieve racial equality, nonetheless associate black faces with negative words and white faces with positive words. And there is a growing body of evidence suggesting that these implicit biases also affect our behavior, though we are usually completely unaware that this is happening.

Moral philosophers have long been concerned to characterize the circumstances under which people are reasonably held to be morally responsible for their actions. Are we morally responsible for behavior that is influenced by implicit biases? That’s a question that has sparked heated debate, and it is a question that could not have been asked without the new synthesis.

Will all this still be news in the decades to come? My prediction is that it will. We have only begun to see the profound changes that the new synthesis will bring about in moral philosophy.

When the first close-up pictures of Pluto came in from the New Horizon satellite which flew by the planet this year, they shocked pretty well everyone who had thought about the now-demoted dwarf planet. Common sense suggested Pluto should be a frozen ball, with a pockmarked surface reflecting billions of years of Comet impacts. Instead what was revealed was a dynamic object, with mountains 3-4 kilometers high, and a huge 1000 km wide flat plain of ice with no impact craters. This means that this plain cannot be older than 100 million years old, implying that the surface of Pluto is dynamic. Since there are no other large planets nearby that might be sources of tidal heating, this means that Pluto still has an active internal engine, continuing to mold its surface. We have no idea how that could be the case.

Similar surprises have accompanied flybys of other solar system objects, from the liquid water ocean, and the organic-water geysers pushing through the surface ice of Saturn’s Moon Encedalus, to the surface full of volcanoes on Jupiter’s Moon Io. While these oddities are now understood to be powered by the huge tidal influence of the giant host planets, no one had expected this kind of extreme activity in advance.

As we peer out further to other stars we have found them to be ripe with planetary systems that were once thought to be impossible. Large gas giants like Jupiter and Saturn orbiting closer in to their stars than Mercury is to our Sun. It had previously been felt that inner planets would be small and rocky and outer planets larger and gaseous, as in our solar system. We now understand that dynamical effects may have caused large planets to migrate inwards over time in these systems.

Similarly, classical dynamics had suggested that binary star systems should not contain planets, as gravitational perturbations would expel such orbiting objects in a short time. But planets have now been discovered around even binary stars, suggesting some new stabilizing mechanism must be at work.

We are accustomed to recognizing that at the extremes of scale, the Universe is a mysterious place. For example, Dark Energy—the energy of empty space—appears to dominate the dynamics of the Universe on its largest scales, producing a gravitational repulsion that is causing the expansion of the Universe to accelerate. Or, on small scales, we currently have no idea why the newly discovered Higgs particle is as light as it is, one of the reasons the four forces in nature have the vastly different strengths we measure on laboratory scales.

But what we are learning as we explore even our own solar neighborhood, is that the physics governing the formation and evolution of nearby planetary-scale objects like Pluto, Io, and Enceladus—physics that we thought was well understood—is actually far richer and more complex than we had ever imagined. This not only brings the lie to claims made decades ago that physics was over, that no new results of relevance to understand human-scale physics would ever again occur, it also puts in perspective the hyperbolic claims made that a quantum theory of gravity such as the most popular candidate, superstrings or M-Theory, would be a Theory of Everything. While such a theory would be of vital importance for understand the origin of the Universe, and the nature of space and time, it would be irrelevant for understanding complex phenomena on human scales, like the boiling of oatmeal, or formations of sand on the beach.

While oatmeal and sand may not capture the public’s imagination, the exotic new worlds inside and outside our solar system certainly do. And our recent discoveries suggest that much conventional wisdom about even our nearest neighbors, and physics as classical as Newton’s, will have to be rethought. The result of such revisions will likely shed new light on vital questions including the big one: Are we alone in the Universe? It is hard to see how our cosmic backyard could get more interesting!

They say that news serves as the first draft of history, and that reportage is just literature in a hurry. Both history and literature have more patience and perspective than the often urgent work of science. So, what’s new and what is news in the special domain of science? For me, the important news in this area is about news itself and the relation between news and science. The most important news about science is how transparent it is becoming.

News is both socially constructed, and it is the construction of the social.

It is socially constructed in that it is contextual, subjective, and ephemeral. And it constructs the social in that the work of news is to tell us who and what we are. We’ve known the social construction aspect of news about politics and power since Plato’s cave. Now, we are learning to recognize more of the interplay between science and social construction with the emergence of more transparent, open science news. Through the news about science news, we are learning how much science is socially constructed, and how to deal with this fact.

The most important changes with regard to news are, themselves, social. News, including science news is collected, collated, curated, and consumed by ever-growing circles of stakeholders. During our lifetime, or even the past decade, the Science-News-Society axis has been redrawn entirely. Instead of being a trickle down, so called “broad”-cast experience, news is now a bottom-up phenomenon. Fewer "invisible colleges," and many more public arenas for science. News about discoveries, innovations, controversies and evidence are increasingly grass-roots generated and ranked, and universally more accessible. Economics of tuition and budget play a role, as does the evolving perception of the structure of knowledge.

Quite a few factors come to play in forming these developments. Literacy is up, censorship is down. Access is up. Uniformity and control over news sources are down, even though algorithmic news curation and ranking are up. Thus, through the news about science and the new ways of such news, expectations for the democratization of science, its funding and fruits, are all up. In fact, this venue, the public and cross-disciplinary conversations here on Edge, are one pleasant and prime example.

While attempts to control or filter news, including the news about science, have not slowed down, the actual ability of regimes and authorities to put a lid on public knowledge of events and discoveries is falling apart. Sharing, in all its online forms, is up. Science news is a major case in point with regards to sharing. The boundaries between scientific publishing and news enterprises are eroding. In this open and transparent environment, anti-intellectual and non-scientific phenomena such as conspiracy theories are less likely to hold for very long.

Truth just might have a chance.

This is not necessarily all good news. I state all of these not because we should let our guard down. Problems and challenges are at both the high and the low end. More transparent and participatory science may mean too much populism. Critical thinking about the organs and channels of news dissemination should continue. At the other, “high” end, monopolies still loom, not the least of which in scientific publishing. Concentrated ownership of media outlets is still a threat, and in some locations growing. Attempts to manipulate the reporting of news, scientific literature and learning curricula in the service of an ideology, power that be or plain interests have not gone away. The loss of some traditional venues for news, the erosion of business models for others, alongside the problems experienced by some of the scientific dissemination are a continuing cause for concern. But this is a transitional period, and the transition is in the right direction.

Whether the first draft of history, or just “literature in a hurry,” the important news is more in the eye of the beholder than set in stone. Thus, the most encouraging news about science is that there are many more eyes beholding, ranking, participating and reacting to the news of science.

Climate collapse demands a supply of energy that is far cheaper than fossil fuels, resistant to bad weather and natural disaster, and sustainable in fuel inputs and pollution outputs. Can a new poorly understood technology from a stigmatized field fulfil the need? The Low Energy Nuclear Reaction (LENR) could help at large scale very quickly.

In 1989 Pons and Fleischmann provided an initial glimpse of an unexpected and poorly understood reaction dubbed "cold fusion," which makes lots of heat and very little radiation.

LENR is being pursued quietly by many large aerospace companies, leading automakers, startup companies and to a lesser extent, national labs.

Over the years many teams have observed the reaction by various means, and a consistent, though unexpected, pattern has emerged. Experiments have become more repeatable, more diverse, more unambiguous, and higher in energy.

There are no expensive or toxic materials or processing steps, so it could be the step beyond fossil fuels we have been waiting for. No government regulated materials are used, so a quick path to commercialization is possible.

Familiarity with hot fusion led to initial false expectations. Early very hasty replication work at MIT was declared a failure when heat but no high energy neutrons were detected. The reaction requirements were not known at first and many attempts failed to reach fuel loading and ignition energy requirements. Even when the basic requirements were met, nano-scale features varied in materials and made the reaction hard to reproduce. Pons & Fleischmann had trouble repeating their own excess energy results after they used up their initial lucky batch of palladium. Today we understand better how material defects create required high energy levels.

In many experiments with LENR, observed excess heat drastically exceeds known or feasible chemical reactions. Experiments have gone from milliwatts to hundreds of watts. Ash products have been identified and quantitatively compared to energy output. High energy radiation has been observed, and is entirely different than hot fusion.

Dr. McKubre at SRI International teased out the required conditions out of the historical data. To bring forth LENR reactions that produce over-unity energy, a metal lattice heavily loaded with Hydrogen isotopes, driven far out of equilibrium by some excitation system involving proton flux and probably electromigration of lattice atoms as well.

A great quantitative characterization of the outputs was Dr. Miles' meticulous 1995 experiment at China Lake. LENR releases Helium-4 and heat in the same proportion as familiar hot fusion, but neutron emissions and gamma rays at least 6 orders of magnitude less than expected.

Successful excitation systems included heat, pressure, dual lasers, high currents and overlapping shock waves. Materials have been treated to create and manipulate flaws, holes, defects, cracks, and impurities, increase surface area, and provide high flux of protons and electron current. Solid transition metals host the reaction, including Nickel and Palladium.

Ash includes ample evidence of metal isotopes in the reactor that have gained mass as if from neutron accumulation, as well as enhanced deuterium and tritium. Tritium is observed in varying concentrations. Weak X-rays are observed along with tracks from other nuclear particles.

LENR looks like fusion judging as a chemist might, by the inputs Hydrogen and output Helium-4 and transmutation products. It looks not-at-all like fusion when judging it as a plasma physicist might—by tell-tale radioactive signatures.

Converting Hydrogen to Helium will release lots of energy no matter how it is done. LENR is not zero-point energy or perpetual motion. The question is whether that energy can be released with affordable tools.

Plasma physicists understand hot thermonuclear fusion in great detail. Plasma interactions involve few moving parts, and the environment is random so it's effect is zeroed out. In contrast, modeling the LENR mechanism will involve solid-state quantum mechanics in a system of a million parts, being driven far out of equilibrium. In LENR a nano-scale particle accelerator can't be left out of the model. A theory for LENR will rely on intellectual tools that illuminate x-ray lasers or high temperature superconductors or semiconductors.

Many things need to be cleared up. How is the energy level concentrated enough to initiate a nuclear reaction? What is the mechanism? How do output energies in the MeV range come out as obvious high energy particles? Dr. Peter Hagelstein at MIT has been working hard at a "Lossy Spin Boson Model" for many years to cover some of these gaps.

Robert Godes at Brillouin Energy suggests a theory that matches observations and suggests an implementation. The "Controlled Electron Capture Reaction." Protons in a metal matrix are trapped to a fraction of an Angstrom under heat and pressure. A proton can capture an electron and become an ultra-cold neutron that remains stationary, but without the charge. That allows another proton to tunnel in and join it, creating heavier Hydrogen and heat. That creates deuterium which goes to tritium to Hydrogen-4. Hydrogen-4 is new to science and is predicted (and observed?) to beta decay to Helium-4 in about 30 milliseconds. All this yielding about 27 meV in total per atom of Helium-4, as heat.

The proton-electron capture reaction is common in the sun, and predicted by super-computer simulation at PNNL. It is the reverse of free-neutron beta decay. Such a reaction is highly endothermic- absorbing 780 keV from the immediate surroundings.

Fission experts expect hot neutrons to break fissile atoms up. LENR does it backwards—ultra cold neutrons (which cannot be detected by neutron detectors, but can readily be confirmed by isotope changes) are targets for Hydrogen.

Hence Helium is produced with the tools of chemistry and without overcoming the Coulomb positive-particle repulsion force. And without requiring or producing radioactive elements.

It is strange that LENR is neglected by the DOE, industry and the Pentagon. But no stranger than the history of nuclear power—if it weren't for the leadership Admiral Rickover, and his personal friends in Congress, nuclear fission power for submarines and power plants would never have seen the light of day. The best endowed institutions rarely disrupt the status-quo.

Progress is being made quickly by private enterprise in lieu of government support. Sadly that means you cannot stay up to date relying on a subscription to "Science." But stay tuned.

Suppose a team of researchers discovers that people who earn $50,000 a year are happier than people who earn $30,000 a year. How might the team explain this result?

The answer depends largely on whether the team adopts a telephoto zoom lens or a wide-angle lens. A telephoto zoom lens focuses on narrower causes, like the tendency for financial stability to diminish stress hormones and improve brain functioning. A team that uses this lens will tend to focus on specific people who earn more or less money each year, and any differences in how their brains function and how they behave. In contrast, a team that adopts a wide-angle lens will focus on broader differences. Perhaps people who earn more also live in safer neighborhoods with superior infrastructure and social support. Though each team adopts a different level of analysis and arrives at a different answer, both answers can be right at the same time.

For decades and even centuries, this is largely how the social sciences have operated. Neuroscientists and psychologists have peered at individuals through zoom lenses, while economists and sociologists have peered at populations through wide-angle lenses.

The big news, of late, is that these intellectual barriers are dissolving. Scientists from different disciplines are either sharing their lenses or working separately on the same questions, and then coming together to share what they've learned. Not only is interdisciplinary collaboration on the rise, but papers with authors from different disciplines are more likely to be shared and cited by other researchers. The benefits are obvious. As the income gap example shows, interdisciplinary teams are more likely to answer the whole question, rather than focusing on just one aspect at a time. Instead of saying that people who earn more are happier because their brains work differently, an interdisciplinary team is more likely to compare the role of multiple causes in formulating its conclusion.

At the same time, researchers within disciplines are adopting new lenses. Social and cognitive psychologists, for example, have historically explored human behavior in the lab. They still do, but many prominent papers published this year also include brain imaging data (a telephoto zoom lens), and data from social media sites and large-scale economic panels (wide-angle lenses). One paper captured every word spoken within earshot of a child during the first three years of his life to examine how babies come to speak some words faster than others. A second paper showed that research grant agencies favor male over female scientists by examining the content of thousands of grant reviews. And a third analyzed the content of 47,000 tweets to quantify expressions of happiness and sadness. Each of these methods is a radical departure from traditional lab experiments, and each approaches the focal problem from an unusually broad or narrow perspective. These papers are more compelling because they present a broader solution to the problem they're investigating—and they're already tremendously influential, in part because they borrow across disciplines.

One major driver of intellectual convergence is the rise of "big data," not just in the quantity of data, but also in understanding how to use it. Psychologists and other lab researchers have begun to complement lab studies with huge, wide-angle social media and panel data analyses. Meanwhile, researchers who typically adopt a wide-angle lens have begun to complement their big data analyses with zoomed-in physiological measures, like eye-tracking and brain imaging analyses. The big news here is not just that scientists are borrowing from other disciplines, but also that their borrowing has turned over richer, broader answers to a growing range of important scientific questions.

Can something that didn't happen be news? The scientific field could be open for this paradox if one considers that science can generate new knowledge, but it is also able to generate new questions. Can something that didn't happen be interesting and important? In order to find an answer, one needs to add a duration to the notion of news. The answer cannot be about facts happening in a particular moment in time, but it can very well be about "news that will stay news." News that has consequences.

"Big news" is news that succeeds in framing the debate, news that is often controversial. It is always interesting, and only sometimes it is important. It’s popularity doesn't mean that it is "news that will stay news," because it needs to be both interesting and important.

"News that will stay news" is different from "big news": it can be under-reported, but it will last for a long time. This means that it is probably more than news about facts; it is a story with a long lasting effect on many facts. It is a story that makes history, at least for a while. It is a narrative that guides human choices in their building the future. It is very rare to find news about the emergence of a new narrative. Newspapers are not made to do that. It is more probable to read that kind of emergence, not in the news, but between the lines of the news. It can be a story about a fact that didn't happen but is interesting and important. It can be on the order of a "black hole of the news."

Some kind of context is needed to go through this matter. While science has changed human life so much, it has also been able to evolve in the news-making field. Notions such as "climate change," "gene-editing," and "nanotechnology" have given a popular brand to a set of important research paths that otherwise would have appeared less interesting, maybe unnoticed or even misunderstood. This new ability has improved science's funding and political relevance.

But the convergence of science and communication is not enough to deal with the "great transformation" that the world is facing, which needs "aware audiences," but even more, it needs "informed citizens." The very notion of a "science based policy" needs an improvement. A quite simple definition of this notion has lead to some better informed decisions in fields such as health and education, but it is still differently understood in different political and cultural context, particularly those in which ideology and religion count a lot in the decision making process. But while many local affairs need to be decided on the basis of the different cultures, some planetary matters need a common understanding of problems and possible solutions.

The United Nations Conference on Climate Change in Paris was an example of a winning relationship between science and policy, even though it took too long to happen and it achieved too little. Politicians will always be responsible for the decision making, but urgent global problems often need a better quality of "science based policy." And it is not only a question of politicians listening more to science; it is also a question of science that is more effective in self-governing, making the news to develop more "informed citizens" and thus help decision makers in taking the best path.

“Gene-editing” provides an important case study. The U.S. National Academy of Sciences, the National Academy of Medicine, the Chinese Academy of Sciences, and the U.K.'s Royal Society recently co-hosted in Washington a summit with international experts to discuss the scientific, ethical, and governance issues associated with human gene-editing research. The idea was to call for a moratorium on using the CRISPR-Cas9 technology to edit the human genome in a permanent and heritable way.

Between the scientists calling for a moratorium there were the very inventors of the CRISPR-Cas9 technology, which has been exponentially successful because it made easy and cheap to "edit" the genome. The moratorium was to be called because unintended consequences were to be expected if using CRISPR-Cas9 for human germline editing. But the summit ended with no big decisions. The national academies opted for a continuing discussion.

The CRISPR-Cas9 summit was not an epic event. One winning argument for not making decisions was brought up by George Church, a researcher at Harvard interested in this debate because he works in the field of human gene editing: he opposed the idea of a ban on human gene editing by arguing that it would strengthen underground research, black markets, and medical tourism, suggesting that science in a globalized economy is pretty much out of control. This is the kind of story that one reads behind the lines in the news.

In fact, the debate about artificial intelligence lead by Stephen Hawking was also about science going out of control. Some discussions about robots that can take over human jobs are about science going out of control, too. Science facts and news are creating a big question mark: is science out of control? Could it be different? Could a sort of science exist that was under control?

The old way to answer was more or less the following: science is about finding out how things are; ethics or policy will serve the purpose of deciding what to do about them. That kind of answer does not help anymore, because science is very much able to change how things are. It is self-governing and, in this regard, science decides about human life, while the growing demand for a science based policy enables science to take part in the decision making process. If a scientific narrative converges with both the laissez-faire ideology and the idea of complexity, the decision making process becomes more and more difficult and the situation seems to go out of control.

Science needs to do something about this. Ethics helps individual decision making but it needs an idea about complexity. Policy takes collective decisions but it needs theories about the way the world is changing. Science is called to take part of the decision making. But how is it going to do it without losing its soul?

There cannot be a science under control. But there can be a science that knows how to empirically deal with choices and gets better at self-government. The piece of "news that will stay news," this year could be the fact that scientists were not able to decide about human gene editing: it is a story that will stay news until when an improved "science of the consequences" story is begun. This means that the scientific method is called to take into account the consequences of the research. If the decision making process is no more the field of only ethics and politics, epistemology is called to get into action.

In August 2015, Brian Nosek and the Open Science Collaboration published a report on the replicability of findings previously published in top rank psychology journals:

“We conducted replications of 100 experimental and correlational studies... using high-powered designs and original materials when available.”

Only 36 percent of the replications were "successful." Among the findings that didn't replicate were these:

“People are more likely to cheat after they read a passage informing them that their actions are determined and thus that they don't have free will.”

“People make less severe moral judgments when they've just washed their hands.”

“Partnered women are more attracted to single men when they're ovulating.”

These particular findings may not be game-changing. But they have been widely cited by other researchers (including me).

In many cases there may well be innocent explanations for why the original study gave the unreliable results it did. But in more than a few cases it can only be put down to slipshod research, too great haste to publish, or outright fraud. Worryingly, the more newsworthy the original finding, the more likely it could not be replicated. Insiders have likened the situation to a “train-wreck.”

John Brockman likes to quote Stewart Brand:

“Science is the only news. When you scan through a newspaper or magazine, all the human interest stuff is the same old he-said-she-said, ... a pathetic illusion of newness. Human nature doesn't change much; science does.”

But we have here a timely reminder that the distinction between science and journalism is not—and has never been—so clear-cut as Brand imagines.

The reality is that science itself has always been affected by “this human interest stuff.” Personal vendettas, political and religious biases, stubborn adherence to pet ideas have in the past led even some of the greatest scientists to massage experimental data and skew theoretical interpretations. Happily, the body of scientific knowledge has continued to live and grow despite such human aberrations. In general scientists continue to play by the rules.

But we must not be complacent. The professional culture is changing. In many fields, and not of course only in psychology, science is becoming more of a career path than a noble vocation, more of a feeding trough than a chapel of truth. Sub-prime journals are flourishing. Bonuses are growing. After the disgrace of the bankers, science must not be next.

Throughout the history of the United States, white people have been its dominant ethnic group. The exact definition of this "race" has changed over time, as successive waves of immigrants (Germans in the 18th century, Irish in the 19th, Italians and Jews in the late 19th and early 20th) worked to be included in the privileged category (as recounted, for example, in Noel Ignatiev's How the Irish Became White). Whatever "whiteness" meant, though, its predominance persisted—both statistically (as the absolute majority of the population) and culturally (as the no-asterisk default definition of American). Even today, long after the legal structure of discrimination was undone, advantages attach to white identity when seeking work, housing, education, or in any encounter with authority. Not unrelatedly, life expectancy for whites is greater than for African-Americans.

But this era of white predominance is ending.

Not long after 2040, fewer than half of all Americans will identify as white, and the country will become a "majority-minority" nation—47 percent white, 29 percent Hispanic, 13 percent African-American, and 11 percent "other," according to U.S. Census Bureau projections. Given this demographic shift, the habits and practices of a white-dominated society cannot endure much longer. Political, legal, cultural, and even personal relations between races and ethnic groups must be renegotiated. In fact, this inevitable process has already begun. And that's news that will stay news, now and for a long time to come. It is driving a great deal of seemingly unrelated events in disparate realms, from film criticism to epidemiology.

I'll begin with the most obvious signs. In the past two years, non-whites have succeeded as never before in changing the terms of debates that once excluded or deprecated their points of view. This has changed both formal rules of conduct (for police, for students) but also unwritten norms and expectations. Millions of Americans have recently come to accept the once fringe idea that police frequently engage in unfair conduct based on race. And many now support the removal of memorials to Confederate heroes, and their flag, from public places.

Meanwhile, campuses host vigorous debates about traditions that went largely unquestioned two or three years ago. (It is now reasonable to ask, if Princeton wouldn't name a library after Torquemada, why should it honor the fiercely racist Woodrow Wilson?) The silliness of some of these new disputes (like Oberlin students complaining that the college dining hall's Chinese food is offensive to Chinese people) shouldn't obscure the significance of the trend. We are seeing inevitable ethnic renegotiation taking place before our eyes, as what was once "harmless fun" (like naming your football team the "Redskins") is redefined as a thing no decent American should condone.

It's nice to imagine this process of political and cultural reconfiguring as a gentle and only slightly awkward conversation. But the evidence suggests that the transition will be painful and its outcome uncertain.

Ethnic identity (like religious identity, with which it is often entangled) is easy to modify over time (again, see How the Irish Became White) but difficult to abandon. This is especially true when people believe their numbers and influence are declining. In that situation, they become both more aware and concerned about ethnicity and more hostile to "outsiders." (In a paper published last year, for example, the social psychologists Maureen A. Craig and Jennifer A. Richeson found that white citizens who'd read about U.S. demographics in 2042 were more likely to agree with statements like "it would bother me if my child married someone from a different ethnic background," compared to whites who had read about 2010's white-majority demographics.)

This sentiment can feed a narrative of lost advantage even when no advantage has been lost. Though whites remain privileged members of American society, they can experience others' gains toward equality as a loss for "our side."

Hence the state of American politics in 2016 is also a part of this story-that-will-stay-a-story. The distress of white people over the loss of their predominance—a sense that "the way things were before was better"—has rewarded frankly xenophobic rhetoric, and the candidates who use it. As the journalist Evan Osnos has reported, outright white supremacists (who usually ignore two-party political races) are delighted.

We should not imagine though that this distress among some whites is written merely in rhetoric. In a recent analysis of statistics on sickness and death rates, the economists Anne Case and Angus Deaton found that middle-aged white people in the United States have been dying by suicide, drug abuse and alcohol-related causes at extraordinary rates. The historian and journalist Josh Marshall has pointed out that this effect is strongest among the people who, lacking other advantages, had the most stake in white identity: less-educated, less skilled, less affluent workers. (Other scholars have disputed details of Case and Deaton's analysis, but not its overall point.)

If the Case-Deaton statistics reflected only economic distress, then middle-aged working-class people of other ethnic groups should also be missing out on the general health improvements of the last few decades. This is not the case. Unskilled middle-aged African-Americans, for example, have lower life expectancy than equivalent whites. Yet their health measures continually improved over the time period during which those of whites stalled.

For this reason, I think Marshall is right, and that the Case-Deaton findings signal a particularly racial distress. The mortality rates correlate with loss of privilege, unspoken predominance, a once undoubted sense that "the world is ours."

What all this suggests is that this ongoing news, over the next ten or twenty years, could turn into a grim story of inter-ethnic conflict. There is a reason those white supremacists, eager for a conflict fought on race lines, are taking a new interest in conventional politics.

Can scientists and other intellectuals do anything to help prevent this inevitable ethnic reconfiguration from being interpreted as a zero-sum conflict? I think they can.

For one thing, there is much that is not known about how the psychology and even physiology of loss of ethnic advantage. There is probably much that could be learned by systematic comparative research on societies in which relations among social groups were swiftly renegotiated, so that one group lost privilege. South Africa after the fall of apartheid is one such place; perhaps Eastern Europe during and after the fall of Communism is another.

We could also sharpen up our collective understanding of the slippery psychology of ethnic threat, with an eye toward finding methods to understand and cope with such feelings. To do that, we need to take people's perceptions about identity seriously. Happy talk about the wonders of diversity and the arc of history bending towards justice will not suffice. We need to understand how, why and when some people on this inevitable journey will experience it as a loss.

Terrorism has indeed caused a huge death toll in countries such as Afghanistan, Syria, and Nigeria. But in Europe or North America a terrorist attack is not what will likely kill you. In a typical year, more Americans die from lightning than terrorism. A great many more die from second-hand smoke and "regular" gun violence. Even more likely, Americans can expect to lose their lives from preventable medical errors in hospitals, even in the best of them. The estimated number of unnecessary deaths has soared from up to 98,000 in 1999 to 440,000 annually, according to a recent study in the Journal of Patient Safety.

Why are we scared of what most likely will not kill us? Psychology provides us with an answer. It is called fear of dread risks. This fear is elicited by a situation in which many people die within a short time. Note that the fear is not about dying, but about suddenly dying together with many others at one point of time. When as many—or more—people die distributed over the year, whether from gun violence, motorcycle accidents, or in hospital beds, it is hard to conjure up anxiety.

For that reason terrorists strike twice. First with physical force, and second by capitalizing on our brains, that is, our propensity for dread risk fear. After 9/11, many Americans avoided flying and used their cars instead. As a consequence, some 1,600 people died from the resulting automobile accidents, which is more than the total number of individuals who were killed aboard the four hijacked planes. That can be called Osama bin Laden’s second strike. All those people could still be alive if they had flown instead of driven, seeing as there was no single deadly accident on commercial airline flights in the US for a number of years thereafter.

Although billions have been poured into Homeland Security and similar institutions to prevent the first strike of terrorists, almost no funding has been provided to prevent the second strike. I believe that making the general public psychologically aware of how terrorists exploit our fears could save more lives than NSA big data analytics. It could also open people's eyes to the fact that some politicians and other interest groups work on keeping our dread risk fear aflame to nudge us into accepting personal surveillance and restriction of our democratic liberties. Living in terror of terrorism can be more dangerous than terrorism itself.

John Donne wrote these words almost 400 years ago and, aside from the sexism of the male pronoun, his words are as true now as they were then. I believe they will be just as true in the future, and apply to scientific discovery as well as to philosophy: the interconnectedness of humans, and of humans and their environment that science is demonstrating today is just the beginning of what we will discover and is the news very likely to be discussed in the future. A few examples will suffice to explain my reasoning.

From the science of economics to that of biology, we are learning how the actions and decisions of each and every one of us affect the lives of all others. Maybe it is no surprise that the coal-fired energy plants of India, China, and elsewhere affect the climate of us all, as does the ongoing deforestation of the Amazon. Or that a nuclear disaster in Japan shaped how we view one alternative energy source. But we now know that our health (particularly our microbiome) is affected not only by what we put into our mouths but, somewhat surprisingly, also by the company we keep. Recent studies show that decisions about the removal of an invasive species affects its entire surrounding ecological web as much as decisions concerning the protection of an endangered one.

One need not necessarily buy into Donne’s somewhat dark worldview to appreciate the importance of his words. Interconnectedness means that the scientists of the world work to find a cure for a disease such as Ebola that has, so far, primarily been limited to a few countries. It also means that governments recognize how reacting to the plight of refugees from war-torn areas halfway around the globe could be a means of enriching rather than impoverishing one’s country.

Whether we look at social media, global travel, or any other form of interconnectedness, news of its importance is here to stay.

We are entering the Age of Awareness, marked by machine intelligence everywhere. It is a world instrumented with sensors that constantly describe the location and state of billions of people and objects, transmitting, analyzing, and sharing this information in cloud computing systems that span the globe. We are aware of innumerable interactions, and increasingly capable of statistically projecting outcomes.

The scientific breakthroughs will depend not just on these tools, but equally on the system into which they are integrated. The biggest changes and breakthroughs from the instrumented world bring together once disparate sectors of computing which, by working in unison, create new approaches to product design, learning, and work.

The sectors include mobility, sensors, cloud computing, and data analysis, whether by machine learning or artificial intelligence. Sensors don’t just give us new information about nature and society, they inform the configuration of cloud systems, and the behavior of the analysis algorithms is likewise affected by the success with which it alters the other two.

The result is a kind of flywheel world, in which data that was once stored and fetched now operates in streams, perpetually informing, changing, and being changed. The accelerating rate of change and increasing pace of discovery noted by many is a result of this shift. On a pragmatic level, it means that we will design much of the world to be in potential state, not a fixed one, since its value is also derived from interaction. On a somewhat more philosophical level, it is the end of the 2,500-year-old (and increasingly suspect) Aristotelian project of creating a state of final knowledge.

Inside this system, the eternal present of consciousness within a solitary self is being modified by a highly connected and global data storage of the past, computation of the present, and statistical projection of the future.

We already see our human habits changing with the new technology, much the way print once reoriented political and religious consciousness, or society changed to suit industrial patterns. As people, we are starting to imitate a software-intensive cloud computing system. Billions of people are gaining near-infinite capabilities to communicate across languages to billions of other people. Artificial ntelligence agents resident within those systems will track people, learning and assisting them, and to yet-unknown extents reporting on the individuals to corporate (and possibly government) masters.

Learning is increasingly a function of microcourses that teach what you need to know, and thanks to analysis in the system, what you need to know next. We perceive life’s genetic code as an information system, and are learning how to manipulate it, either to hack the human body or to use DNA for unimaginably small and powerful computers that could extend greatly the powers of awareness and control.

Unique among times when technology has changed ideas about the world, this Age of Awareness knows that it is remaking the consciousness and expectations of being human. Guttenberg in 1450, or an industrialist in 1810, had no awareness of an effect on humans wrought by new technologies. Everyone now building the instrumented, self-aware planet can see and analyze the effect of their labor. That does not, to date, significantly improve our ability to plan or control its outcomes.

Cancer is often described as a sped-up version of Darwinian evolution. Through a series of advantageous mutations, the tumor—this hopeful monster—becomes fitter and fitter within the ecosystem of your body. Some of the mutations are inherited while others are environmental—the result of a confusion of outside influences. Much less talked about is a third category: the mutations that arise spontaneously from the random copying errors occurring every time a cell divides.

In a paper this year in Science, Cristian Tomasetti and Bert Vogelstein calculated that two-thirds of the overall risk of cancer may come from these errors—entropic "bad luck." The paper set off a storm of outrage among environmentalists and public health officials, many of whom seem to have misunderstood the work or deliberately misrepresented it. And a rival model has since been published in Nature claiming to show that, to the contrary, as much as 90 percent of cancer is environmentally caused. That to me is the least plausible of these dueling reports. As epidemiology marches on, the link between cancer and carcinogen seems ever fuzzier. The powerful and unambiguous link between smoking and lung cancer almost seems like a fluke.

It will be interesting to see how this plays out. But meanwhile I hope that more of the public is beginning to understand that getting cancer usually doesn't mean you did something wrong or that something bad was done to you. Some cancer can be prevented and some can be successfully treated. But for multicellular creatures living in an entropic world, a threshold amount of cancer is probably inevitable.

The most notable scientific news story in 2015 was not obviously about science.

What was apparent was the coverage of diverging economic realities. Much of the world struggled with income inequality, persistent unemployment, stagnant growth, and budgetary austerity, amid corporate profit records and a growing concentration of wealth. In turn, this gulf led to a noisy emergence of far-right and far-left political movements, offering a return to a promised better time from decades (or centuries) ago. And these drove the appearance of a range of conflicts, connected by a common thread of occurring in failing and failed economies.

So what do all these dire news stories have to do with science? They share an implicit syllogism that’s so obvious it’s never mentioned: opportunity comes from creating jobs, because jobs create income, and inequality is due to the lack of income. That's what's no longer true. The unseen scientific story is to break the historical relationship between work and wealth by removing the boundary between the digital and physical worlds.

Some discoveries arrive as an event, like the flash of a light bulb; some are best understood in retrospect as the accumulation of a body of work, where the advance is to take it seriously. This is one of those. Digitizing communication and computation required a few decades each, leading to a revolution in how knowledge is created and shared. The coverage now of 3D printing and the maker movement is only the visible tip of a much bigger iceberg, digitizing not just design descriptions for computer-controlled manufacturing machines (which is decades old), but digitizing the designs themselves by specifying the assembly of digital materials.

Life is based on a genetic code that determines the placement of 20 standard amino acids; that was discovered (by molecular biology) a few billion years ago. We’re now learning how to apply this insight beyond molecular biology; emerging research is replacing processes that continuously deposit or remove materials with ones that code the reversible construction of discrete building blocks. This is being done across disciplines and length scales, from atomically-precise manufacturing, to whole-genome synthesis of living cells, to the three-dimensional integration of functional electronics, to the robotic assembly of modular aircraft and spacecraft. Taken together, these add up to programming reality—turning data into things and things into data.

Returning to the news stories from 2015, going to work commonly means leaving home to travel to somewhere you don’t want to be, to do something you don’t want to do, producing something for someone you’ll never see, to get money to pay for something that you want. What if you could instead just make what you want? In the same way that digitizing computing turned information into a commodity, digitizing fabrication reduces the cost of producing something to the incremental cost of its raw materials.

In the largest-ever gathering of heads of state, the Sustainable Development Goals were launched at the UN in 2015. These target worthy aims including ending poverty and hunger, ensuring access to healthcare and energy, building infrastructure, and reducing inequality. Left unsaid is how to accomplish these, with an assumption that it will require spending vast amounts of money to meet them. But development does not need to recapitulate the industrial revolution; just as developing countries have been able to skip over landlines and go right to mobile phones, mass manufacturing with global supply chains can be replaced with sustainable local on-demand fabrication of all of the ingredients of a technological civilization. This is a profound challenge, but it’s one with a clear research roadmap, and is the scientific story behind the news.

Finding something new in psychology is always easy. The journals are overflowing with reports of gripping new findings, many of which invite continued reflection. But a problem has recently come to the fore: Many of these findings probably won’t replicate. In fact, many of the findings already in the literature have turned out not to replicate—they were statistical flukes, not accurate insights into the nature of the mind and behavior. But this does not mean that we should throw up our hands and walk away from the field. Rather, many findings have turned out to be robust, and many have deep implications that will continue to stimulate and inform.

In this piece I reflect on one such set of findings that has deep implications and will ensure that we will always be presented with something new.

My reflections begin with one of my undergraduate mentors, who was a very senior scientist nearing the end of his long and distinguished career. He once commented to me that even after an extraordinarily close marriage over fifty years, his wife could still think, say, and do things that surprised him. With a little reflection, I suspect that he could have extended the observation: For better or worse, even after a lifetime of living you can still learn something new about yourself that will surprise you.

In my view, this observation is going to be true for all of us. Who and what we are will always have an element of something new, simply because of how the brain works.

Here is the structure of the thinking that led to this conclusion:

The way we respond to objects and situations we perceive or to ideas we encounter (e.g., via speech) depends on our current cognitive state. Depending on one’s current thoughts, feelings and experiences, different concepts are “primed.” Primed concepts are activated in our minds, and tend to take center stage in how we interpret and respond to current situations. A huge literature now documents the effects of such priming.

The way we interpret new stimuli or ideas relies, at least in part, on chaotic processes. Here’s my favorite analogy for this: A raindrop is dribbling down a window pane. An identical raindrop, starting at exactly the same spot on the window at another time, would trace a different path. Even very very small differences in the start state will affect the outcome (this is part and parcel of what it is to be a chaotic system.) The state of the windowpane (which depends on ambient temperature, effects of previous raindrops, and other factors) is like the state of the brain at a particular point in time: Depending on what one has just previously encountered and what one was thinking and feeling, different concepts will be primed. And this priming will influence the effects of a new perception or idea.

Over age and experience, the structure of information stored in long-term memory will become increasingly complex. Hence, priming can have increasingly subtle and nuanced effects, which become increasingly difficult to predict.

In short, each of us grows as we age and experience more and varied situations and ideas, and we will never be able to predict perfectly how we react to a new encounter. Why not? What we understand about ourselves depends on what we paid attention to at the time events unfolded and on the highly imperfect conceptual machinery we have for interpreting ourselves. Our understanding of ourselves will not capture the subtle and nuanced effects of the patterns of priming that affect our immediate perceptions, thoughts and feelings.

In sum, although we cannot be forever young, we can be indefinitely new—at least in part.

Why is this important? This reasoning suggests that we should give others and ourselves some slack. We should be forgiving when friends surprise us negatively—the friends may be surprised themselves. And the same is true for ourselves.

“Then the Lord God formed the man of dust from the ground and breathed into his nostrils the breath of life, and the man became a living creature.” –Genesis 2:7

We humans have always been convinced of our own specialness, certain that we sit at the center of the universe. Not long ago, people uniformly thought themselves to be God’s favorite creation, placed on a newly created Earth, which was orbited by all other celestial bodies. We believed that humans were fundamentally different from other animals, and possessed intelligence that could never be duplicated. These ideas made us feel comfortable and safe, and so were easy to believe. Unfortunately, they were wrong.

Copernicus and Galileo revealed that the Sun—not the Earth—lay at the center of the solar system, Charles Lyell revealed that the Earth was much older than previously thought, and Darwin revealed that humans were not fundamentally different from other animals. Each of these scientific discoveries—especially evolution—are interesting because they challenge the presumed specialness of humans.

Of course, even if people are just apes with large frontal cortices, at least we can claim humans are part of a very special club: that of living creatures. We can marvel at the beauty of life, at the diversity of plants, and animals, and insects, and bacteria. Unfortunately, one recent—and very interesting—theory undermines the specialness of all life.

The MIT physicist Jeremy England has suggested that life is merely an inevitable consequence of thermodynamics. He argues that living systems are simply the best way of dissipating energy from an external energy source; bacteria, beetles and humans are just the most efficient way to use up sunlight. According to England, the process of entropy means that molecules that sit long enough under a heat lamp will eventually structure themselves to metabolize, move, and self-replicate—i.e., to become alive.

Granted, this process might take billions of years, but—in this view—living creatures are little different from other physical structures that move and replicate with the addition of energy, such as vortices in flowing water (driven by gravity) and sand dunes in the desert (driven by wind).

Not only does England’s theory blur the line between the living and the non-living, it also further undermines the specialness of humanity. It suggests that if humans are especially good at something, it is merely using up energy—something we seem to do with great gusto. This kind of uniqueness hardly warms our hearts, but questioning the specialness of humanity is exactly what makes this science interesting.

When fear and tension rise across racial and ethnic divisions, as they have in recent years, genetic arguments to explain behavioral differences can quickly become popular. However, we know racial and ethnic groups may also be exposed to vastly different experiences likely to strongly affect behavior. Despite seemingly inexhaustible interest in the nature/nurture debate, we are only starting to learn how the interaction of genes with experience may alter the potential of individuals, and to see how individual decision-making styles can alter the potential wealth of nations.

A captivating news image I saw this year depicted two sets of identical twins, mixed up as infants and raised in separate families in Colombia. The boys and their families has assumed they were fraternal twins, who share genes only as much as siblings do and therefore don't look alike. Only in adulthood, did the young men discover the mistake and find their identical twin brothers through the recognition of friends. One mixed pair of twins grew up in the city and the other in countryside with far more modest resources. We are all, of course, eager to know how these different environments altered these men’s personalities, preferences, intelligence, and decision making when their genes were the same. We are all probably fairly comfortable with the idea that trauma, hardship, or parenting style could impact our emotional development and our emotional patterns even in adulthood. However, it is less clear how early experience might affect how we think and make decisions. Is one identical twin more likely to save his money, repeat a mistake, take a short cut, buy lottery tickets, or stubbornly resist changing his mind because he was raised in a different situation? Or would the identical twin pair make the same choice regardless of upbringing? The answers from these twins are still emerging and the sample is, of course, anecdotally small. If we definitively knew the answer to these questions we might change how we view parenting and investment in childcare and education.

A growing body of work now effectively models this "twins raised apart" situation in genetically identical strains of inbred mice or genetically similar rats. Rodents get us away from our cultural biases and can be raised in conditions that model human experience of adversity and scarcity in infancy and childhood. In one early life stress model, the mother rodent is not given adequate nesting material and moves about the cage restlessly, presumably in search of more bedding. In other models, the rodent pups are separated from the mother for portions of the day or are housed alone after weaning. These offspring are then compared to offspring that have been housed with adequate nesting material and have not been separated more than briefly from their mother or siblings.

Investigators first focused early life adversity research in rodent models on emotional behavior. This research found that early life adversity increased adult stress and anxiety-like behavior. More recent studies, including some from my own lab, find that early life adversity can also impact how rodents think—the way they solve problems and make decisions. Rats and mice that have been subjected to greater early life stress tend to be less cognitively flexible (stubbornly applying old rules in a laboratory task after new rules are introduced) and they may also be more repetitive or forgetful. Some of the differences in behavior have been found to fade as the animals get older but others grow stronger and persist into adulthood.

I would hesitate to say that one group was smarter, since it is hard to determine what would be an optimal style for a wild rodent. We might see stubborn, inflexible or repetitive behavior as unintelligent in a laboratory test. In some real world situations, the same behavior might become admirable as “grit” or perseverance.

Competing theories attempt to explain these change in emotional behavior, problem solving, and decision making. The brains of the mice that experienced adversity may be dysfunctional, in line with evidence of atrophy of frontal neurons after stress. Alternatively, humans and rodents may show positive adaptation to adversity. In one model currently growing in popularity, a brain developing under adversity may adopt a "live fast-die young" strategy that favors earlier maturation and short term decision making. In this adaptive calibration model, animals that are genetically identical might express different set of genes in their brain and develop different neural circuits in an attempt to prepare their brain for success in the kind of environment in which they find themselves. It is unclear how many different possible trajectories might be available or when or how young brains are integrating environmental information. However, based on this adaptive calibration model, in lean times versus fat times you might expect a single species to "wire-up" different brains and display different behaviors without requiring genetic change or genetic selection at the germline level (level of eggs and sperm).

Why should we care? These data might explain population level economic behavior and they offer a powerful counter narrative to seductive genetic explanations of success. Another piece of captivating news out this year was Nicholas Wade’s review of Garret Jones’ Hive Mind in the Wall Street Journal. Reading this review, I learned that national savings rates correlate with average IQ scores even if individual IQ scores do not; the subtitle is “How your nation’s IQ matters so much more than your own.” In his review, Wade (not Jones) suggests we need to look to “evolutionary forces” to explain IQ and its correlated behavioral differences. The well controlled data from experiments in mice and rats suggest we also look to early life experience. Rodents are not known for their high IQ, but the bottom line is what intelligence they have is sensitive to early life experience, even when we hold germline genetics constant. Putting these rodent and human findings together, one might hypothesize that humans exposed to instability or scarcity in early life are developing brains that are wired for shorter term investment and less saving. Thus, rather than dismissing the successes and failures of nations to slow changing genetic inheritance, we might foster a brighter future by paying more attention to the quality of early life experiences.

Scientists and engineers continue to make progress in the battle against the overload of confusing choices that plague modern society. In response to well-known studies from the 1990s and 2000s which found that, when presented with too many choices, people often opt to choose nothing at all; efforts in both the research and commercial worlds became focused around mining behavioral “big data.”

Now, intelligent systems can predict what people want before they want it, so rather than showing a choice of navigational options on a website and forcing the consumer to select options and choose among them, the smart app simply shows the two or three choices that are just right for that person. And, rather than scanning all of the news put forward by a reputable news outlet, readers are shown just the right article, personalized for them, so they don’t have to think about how they will stay up to date. Just the movie or video game match you want to watch at this moment appears before your eyes right as you settle into your chair, set at just the temperature you want with just the food you were craving. You don’t even have to give it a second thought! And of course, your voting choices are arrayed out for you in your favorite color scheme.

And it doesn’t stop with reading. Your vacation planning is figured out for you now as well. In the past, before Intelligent Planning, you would never have thought your dream location was a small town in Kansas, but that is indeed your top recommendation, and so of course that is where you and your loved ones will have the best time. This way the people who designed the systems won’t feel crowded in their vacation spots in Kauai.

So, the science news is all good, except for the anti-science Huxley protesters who had contrary thoughts, but that information was not in the essay you wanted to read.

This was the science news essay you wanted to read (based on essays you’ve recently read, thoughts you’ve recently had, the el grande burrito currently in your intestinal tract, and the promotion you did not get last month at work).

And this is the science news essay that I wanted to write. (AIGenerator ™)

Gentle reminder: contrary thoughts experienced during the consumption of this essay will be reported.

The exciting big news for many in the physics community worldwide is that at the collider at CERN an unexpected, very haunting signal of a possible new particle was found, one that would not fit into any part of the current high-energy theory. An entirely new region in experiment and theory might open up, making the finding of the Higgs an old story.

But just after this finding, the collider was shut down, as usual, until next spring. There is now a tantalizing wait. It is, in a way, analogous to the event when the first nuclear reactor in Chicago, in wartime under Enrico Fermi, got to the brink of becoming critical late one morning. But instead of continuing, Fermi asked everyone there to wait until he would return from his mid-day siesta.

The digital revolution progresses at full pace and reshapes our societies. Many countries have invested in data-driven governance. The common idea is that "more data is more knowledge, more knowledge is more power, and more power is more success." This "magic formula" has promoted the concept of a digitally empowered "benevolent dictator" or "wise king," able to predict and control the world in an optimal way. It seems to be the main reason for the massive collection of personal data, which companies and governments alike have engaged in.

The concept of the benevolent dictator implies that democracy would be overhauled. I certainly agree that democracy deserves a digital upgrade, but in recent years many voices in the IT industry have even claimed that democracy is an "outdated technology," which needs to be bulldozed. Similar arguments have been put forward by politicians in various countries. There is now an acute danger that democracy would be ended in response to challenges and threats such as climate change, resource shortages, and terrorism. A number of countries come to mind.

However, recent data-driven analyses show that democracy is not a luxury, in contrast to what has been claimed by increasingly many people before. Democracy pays off. A study by Heinrich Nax and Anke Schorr, using high-performance computers, reveals "short-run economic incentives to de-democratization for the most economically and democratically developed nations. [However,] These short-run boosts come with intermediate-run reductions of political capitals and with long-run reductions in growth." In other words: demolishing democracy would be a terrible and costly mistake.

Therefore, the anti-democratic trend in many countries is dangerous and needs to be stopped. First, because ending freedom, participation, and justice would end in socio-political instability and finally in revolution or war. (Similar instabilities have already occurred during the transition from the agricultural to the industrial society and from there to the service society.) Second, because the above "magic formula" is based on flawed assumptions.

Society is not a machine. It cannot be steered like a car. Interaction—and the resulting complex dynamics of the system—changes everything. We know this, for example, from spontaneous breakdowns of traffic flow. Even if we could read the minds of all drivers, such "phantom traffic jams" could not be prevented. But there is a way to prevent them, based on the use of suitable driver assistant systems: distributed control approaches, using Internet of Things technology, real-time data and suitable real-time feedback, together with knowledge from complexity science.

The paradigm of data-driven optimization would work perhaps if we knew the right goal function; moreover, the world would have to change slowly enough, it would have to be sufficiently well predictable, and simple enough. However, all these preconditions are not fulfilled. As we continue to network the world, its complexity grows faster than the data volume, the processing power and the data that can be transmitted. Many aspects of the world are emergent and hardly predictable. The world is quickly changing by innovation, and we need even more of it! Not even the goal function is well-known: should it be gross national product per capita or sustainability, power or peace, average lifespan or happiness? In such cases, (co-)evolution, adaptation, and resilience are the right paradigms, not optimization.

Decision-makers around the globe need to be aware of these things, to save democracy, to get better information systems on the way than those that are based on mass surveillance and brute-force data mining; to argue for interdisciplinary and global collaboration; for approaches built on transparency and trust; for open and participatory systems, because they mobilize the capacity of the entire society; and for systems based on diversity and pluralism, because they promote innovation, societal resilience, and collective intelligence.

If we don't manage to get things on the right way, we may lose many societal, economic, legal and cultural achievements of the past centuries; we might see one of the darkest periods of human history; something much worse than "1984—Big Brother is watching you": a society, in which we might lose our freedom, enslaved by a "citizen score" that would give us plus or minus points for everything we or our friends and partners do; where the government and big corporations would determine how we should live our lives.

These days, physics seems to find itself in a situation similar to that faced by physicists at the turn of the 19th century, just before the dawn of quantum mechanics and general relativity. In 1894, the co-discoverer of the constancy of the speed of light and first American Nobel laureate in physics, Albert Michelson, stated, “It seems probable that most of the grand underlying principles of physical science have been firmly established.” Many, if not most, physicists at that time believed that the handful of experimental anomalies that seemed to violate those principles were minor details that would eventually be explained by the paradigm of classical physics. Within a generation, history showed that quantum mechanics and Einstein’s theory of relativity had to be invented, and classical physics had to be overthrown, in order to explain those minor experimental details.

In 2012, I spent a year on sabbatical at Princeton due to an invitation by David Spergel, one of the lead scientists of the WMAP space satellite. The satellite was designed to make, at the time, the most precise measurements of the afterglow of the Big Bang, the ripples in the cosmic microwave background radiation (CMB), which according our standard model of cosmology would later develop into the vast structures in our universe, to which there is remarkable agreement. Our best theory of the early universe, cosmic inflation, is armed with the most precise physics known to us, general relativity and quantum field theory. Both theories have been independently tested with beyond hair-thin accuracy.

Despite the success of inflation in predicting a handful of features that was observed by WMAP, similar to the end of the 19th century, some nagging anomalies persist. At Princeton, my colleagues and I spent that year wrestling with those anomalies to no avail. But I forgot about them after a discussion with David who comforted me with the sentiment that the WMAP anomalies are probably due to some unaccounted experimental systematic rather than some strange phenomenon in the sky. David also warned, “if the anomalies persist in the PLANCK satellite, then we will have to take them more seriously.” Well, in 2014 the Planck satellite made an even more precise measurement of the CMB anisotropies and came back with the arguably the most nagging in the suite of anomalies-the hemispherical anomaly. What’s that?

The undulations in the CMB reflect a prediction from cosmic inflation that, aside from those tiny waves, on the largest distance scales the universe looks the same in every direction and at every vantage point in space. This prediction is consistent with one of the pillars of modern cosmology, the Cosmological Copernican Principle. During the epoch that the CMB anisotropies were formed, they too are supposed to, on average look the same in every direction. The theory of cosmic inflation generically predicts this feature. This means that if one divides the sky into two arbitrary hemispheres, we should see the same statistical features of the anisotropies in both hemispheres.

However, both WMAP and Planck see a difference in the amount of anisotropies in different hemispheres in the sky. This feature is in tension one of the most powerful attributes of inflation, whose rapid expansion of space-time smooths out any large-scale directional preference, while democratically sprinkling the space-time fabric with the same amount of ripples in every direction. With some decorative tweaking, it is possible to modify inflation to account for the anomaly, but this seems to be at odds with what inflation was invented for-to make the early universe smooth enough and see the tiny anisotropies that later become galaxies. One might think that this would be a great opportunity for alternative theories of the early universe, such as bouncing/cyclic cosmologies to rise to the occasion and explain the anomalies, but so far, there is no compelling alternative.

Recently, on planet Earth at the Large Hadron Collider in Geneva, the ATLAS, and CMS experiments both reported an “anomaly” when protons collide on energy scales close to 1 trillion electron volts. The experiments see the predicted production of elementary particles that standard quantum field theory of elementary particle interactions predict, with the exception of an excess production of light. Unfortunately, if this observation persists with statistical significance, we will need physics beyond our standard model to explain.

Both of these anomalies, to me and my theoretical colleagues, is a good thing. Will it mean simple, yet less than pretty fix ups of our current “standard models”? Might it be one of our super theories, such as, supersymmetry, strings, loop quantum gravity or GUTS to come to the rescue? Could it be that both anomalies are connected in some yet unseen way? Maybe the anomalies will point us in a direction that our current mode of thinking has yet to do. Whatever it may be, if the anomalies persist, this is exciting times to be a theorist.

At a time when groundbreaking discoveries seem almost commonplace, it is difficult to predict which scientific news is important enough to "stay news" for longer than a few days. To stick around, it would have to potentially redefine "who and what we are." One of the recent scientific advancements that, in my mind, fulfills these prerequisites is decoding and reprogramming DNA via bioinformatics.

While mapping the complete human genome was itself a great achievement, it was bioinformatics that allowed for a practical application of the acquired knowledge. Uploading a genome onto a computer has enabled researchers to use genetic markers and DNA amplification technologies in ways that shed real light on the intricate, otherwise unfathomable gene-environment interactions causing disease.

But like most things in life, there are also possible negative side effects. With DNA being written like software, cloning and "designing" more complex living creatures, including humans, no longer seems a mere fantasy from Sci-Fi movies. All possible advantages aside, it is likely to stir a wide range of ethical debates, requiring us to ponder what it means to be human: a "naturally" conceived, unique, and largely imperfect creature, or a pre-designed, aimed-to-be-perfect being.

Asked about the future of DNA coding and bioinformatics, Craig Venter replied, "We’re only limited by our imagination." I am somewhat more skeptical than this, but one thing is for sure: digital DNA is here to stay in scientific news about evolutionary biology, forensic science, and medicine, and also in debates about the way we, humans, define ourselves.

"Welcome, our new robot overlords," I will say when they arrive. As I sit here nursing a coffee, watching the snow fall outside, I daydream about the coming robot revolution. The number of news articles about robotics and AI are growing at an exponential rate, indicating that superintelligent machines will arise in a very short time period. Perhaps in 2017.

As a roboticist myself, I hope to contribute to this phase change in the history of life on Earth. The human species has recently painted itself into a corner and—global climate conferences and nuclear non-proliferation treaties notwithstanding—seems unlikely to find a way out with biological smarts alone: We’re going to need help. And the growing number of known Earth-like yet silent planets indicates that we can’t rely on alien help anytime soon. We’re going to need homegrown help. Machine help.

There is much that superintelligent machines could help us with.

Very, very slowly, some individuals in some human societies have been enlarging their circles of empathy: human rights, animal cruelty, and microaggressions are recent inventions. Taken together, they indicate that we are increasingly able to place ourselves in others’ shoes. We are able to feel what it would be like to be the target of hostility or violence. Perhaps machines will help us widen these circles. My intelligent pan may suggest sautéed veggies over the bloody steak I’m about to drop into it. A smartphone might detect cyberbullying in a photo I’m about to upload and suggest that I think about how that might make the person in the photo feel. Better yet, we could imbue machines with the goal of self preservation, mirror neurons to mentally simulate how others’ actions may endanger their own continued existence, and the ability to invert those thought processes so that they can realize how their own actions threaten the existence of others. Such machines would then develop empathy on their own. Then, driven by sympathy, they would feel compelled to teach us how to strengthen our own abilities in that regard. In short: future machines may empathize about humans’ limited powers of empathy.

The same neural machinery that enables us (if we so choose) to imagine the emotional or physical pain suffered by another also allows us to predict how our current choices will influence our future selves. This is known as prospection. But humans are also lazy; we make choices now that we come to regret later. (I’m guilty of that right now: rather than actually building our future robot overlords, I’m daydreaming about them instead.) Machines could help us here too. Imagine neural implants that can directly stimulate the pain and pleasure centers of the brain. Such a device could make you feel sick before your first bite into that bacon cheeseburger rather than after you’ve finished it. A passive aggressive comment to a colleague or loved one would result in an immediate fillip to the inside of the skull.

In the same way that machines could help us maximize our powers of empathy and prospection, they could also help us minimize our agency attribution tendencies. If you’re a furry little creature running through the forest and you see a leaf shaking near your path, it’s safer to attribute agency to the leaf’s motion than to not: Better to believe there’s a predator hiding behind the leaf than to just attribute its motion to wind. Such paranoia stands you in good Darwinian stead compared to another creature who thinks "wind" and is eaten by an actual predator. It is possible that such paranoid creatures evolved into religious humans who saw imaginary predators (i.e. gods) behind every thunderstorm and stubbed toe. But religion leads to religious wars and leaders who announce: "God made me do it." Such defenses don’t hold up well in modern, humanist societies. Perhaps machines could help us correctly interpret the causes of each and every sling and arrow of outrageous fortune that we experience in our daily lives. Did I miss my bus because I’m being punished for the fact that I didn’t call my sister yesterday? My web-enabled glasses immediately flick on to show me that bus schedules have become more erratic due to this year’s cut to my city’s public transportation budget. I relax as I start walking to the subway: it’s not my fault.

What a wonderful world it could be. But how to get there? How would the machines teach empathy, prospection, and correct agency attribution? Most likely, they would overhaul our education system. The traditional classroom setting would finally be demolished so that humans could be taught solely in the school of hard knocks: machines would engineer everyday situations (both positive and negative) from which we would draw the right conclusions. But this would take a lot of time and effort. Perhaps the machines would realize that rather than expose every human to every valuable life lesson, they could distil down a few important ones into videos or even text:

The plight of the underdog. She who bullies is eventually bullied herself. There’s just us. Do to others what… Perhaps these videos and texts could be turned into stories rather than delivered as dry treatises on morality. Perhaps they could be broken into small bite-sized chunks, provided on a daily basis. Perhaps instead of hypothetical scenarios, life lessons could be drawn from real plights suffered by real people and animals each day. Perhaps they could be broadcast at a particular time—say, 6pm and 11pm—on particular television channels, or whatever the equivalent venue is in future.

The stories would have to be changed each day to keep things fresh. They would have to be "new." And, of course, there should be many of them, drawn from all cultures, all walks of life, all kinds of people and animals, told from all kinds of angles to help different people empathize, prospect, and impute causes to effects at their own pace and in their own way. So, not "new" then, but "news."

The world is increasingly full of deep architectures—multi-level artificial neural networks used to discover (via "deep learning") patterns in large datasets such as images and texts. But the power and prevalence of these deep architectures masks a major problem—the problem of knowledge-opacity. Such architectures learn to do wonderful things, but they do not (without further coaxing) reveal just what knowledge they are relying upon when they do them.

This is both disappointing (theoretically) and dangerous (practically). Deep learning and the patterns it extracts now permeate every aspect of our daily lives, from online search and recommendation systems, to bank-loan applications, healthcare, and dating. Systems that have that much influence over our destinies ought to be as transparent as possible. The good news is that new techniques are emerging to probe the knowledge gathered and deployed by deep learning systems.

In June, 2015, Alexander Mordvintsev and co-authors published a short piece entitled "Inceptionism: Going Deeper into Neural Networks." Named after a specific architecture, "Inceptionism" was soon trending on just about every geeky blog in the universe. The authors took a trained-up network capable of deciding what is shown in a given image. They then devised an automatic way to get the network to enhance an input image in ways that would tweak it towards an image that would be classified, by that network, as some specific item. This involved essentially running the network in reverse (hence the frequent references to "networks dreaming" and "reverse hallucination" in the blogs). For example, starting with random noise and a target classification, while constraining the network to respect the statistical profiles of the real images it had been trained on, the result would be a vague, almost impressionistic image that reveals how the network thinks that kind of item ("banana," "starfish," "parachute" or whatever) should look.

There were surprises. The target "barbell," for example, led the network to hallucinate two-ended weights all right—but every barbell still had the ghostly outline of a muscular arm attached. That tells us that the network has not quite isolated the core idea yet, though it had gotten pretty close. Most interestingly of all, you can now feed in a real image, pick one layer of your multi-level network, and ask the system to enhance whatever is detected. This means you can use inceptionism to probe and visualize what is going on at each layer of processing. Inceptionism is thus a tool for looking into the networks multi-level mind, layer-by-layer.

Many of the results were pretty psychedelic—repeated enhancements at certain levels resulted in images of fractal beauty, mimicking trippy artistic forms and motifs. This was because repeating the process results in feedback loops. The system is (in effect) being asked to enhance whatever it sees in the image as processed at some level.

So if it sees a hint of birdiness in a cloud, or a hint of faceness in a whirlpool, it will enhance that, bringing out a little more of that feature or property. If the resulting enhanced image is then fed in as input, and the same technique applied, those enhancements make the hint of birdiness (or whatever) even stronger, and another round of enhancement ensues. This rapidly results in some image elements morphing towards repeating, dreamlike versions of familiar things and objects.

If you haven’t yet seen these fascinating images, you can check them out online in the "inceptionism gallery," and even create them using the code available in DeepDream. Inceptionist images turn out to be objects of beauty and contemplation in their own right, and the technique may thus provide a new tool for creative exploration—not to mention suggestive hints about the nature of our own creative processes. But this is not just, or even primarily, image-play. Such techniques are helping us to understand what kinds of things these opaque, multi-level systems know: what they rely upon layer-by-layer as their processing unfolds.

Having long been interested in the probability that cyber-intelligence will soon replace humanity, I could cite frequent news coverage of efforts to produce advanced artificial intelligence as the most important news. But that’s a rather obvious subject, so I won’t.

Instead, I will discuss a much more obscure science news item that has the potential to be of great long-term import. It’s how some privately funded, commercial fusion power projects are being initiated. The intent is to, in the near future, produce the unlimited cheap power that government-backed projects have failed to deliver. The obvious implication is that fusion power could solve the global energy crisis and climate change, but I won’t discuss those items either.

What very few recognize is how the fusion news is tied to a much more prominent story. A major Pew analysis released this year projects a rise in theism in many developing nations in coming decades. This followed a major Pew survey showing a rapid rise of nontheism at the expense of religion in the United States. This has contributed to a common opinion that while religion has been and is continuing to sink in the western democracies, it’s making a comeback in less stable and prosperous nations in a historical rebuff to modernity. The resulting reactionary theism is often adopting a virulent form that afflicts the secular democracies and threatens the future of modern civilization.

What does the news on fusion power have to do with the news of the reactionary religion we’re having to put up with these 21st century days? To begin to see the connection we will start with Arthur C. Clarke.

The SciFi Channel just presented their version of Clarke’s classic novel Childhood’s End. Written in the early 1950s, CE is like many of Clarke’s futurist fictional works in which he repeatedly predicted that in the late 1900s and into the 2000s the world community would become increasingly secular, progressive, and pacific, forming a modernist planetary demi-utopia. This in turn rested on a science-based hope. The technologist Clarke presumed that, like fission power becoming practical in the mid 1900s, fusion energy was a readily solvable science and engineering problem, and that hydrogen to helium reactors would be providing all peoples on our orb all the power they could use by the coming turn of the century. The resulting universal prosperity would elevate all into, at least, the secure, middle class affluence that studies show result in strongly atheistic, liberal, lower violence societies at the expense of often dysfunctional, tribalistic religion.

That has not happened. Fission is easy to achieve at normal surface conditions—so easy that when uranium was more highly enriched back in the Precambrian, reactors spontaneously formed in uranium ores. Sustained fusion thermonuclear reactions so far occur only in the extreme pressure-temperature conditions at the centers of stars, and getting them to work elsewhere has proven extremely difficult. Lacking fusion reactors, we have had to continue rely mainly on fossil fuels that are largely located in regions of ill repute.

Had fusion power come online decades ago, the Saudis would not have had loads of oil-generated cash to fund the virulent Wahhabist mosques and schools around much of the world that have helped spread hyper violent forms of Islam. Lacking cheap fusion power, much of the world remains mired in the lack of economic opportunity that breeds supernaturalistic extremism. Since the end of the Cold War dramatically reduced mass lethal violence from atheistic communists, a few million have died in war level conflicts that share a strong religious component. Muslims are causing the most trouble. But so are Christians in sub-Saharan Africa, as well as Russia, where the Orthodox Church backs Putin. Even the Buddhism Clarke had long seen as peaceful has gone noxious in pasts of Asia, as have many Hindus in India.

But as bad as the situation is, it is not as bad as it may seem. The Pew projections are based on a set of very dubious assumptions, including that the faith people are born into is the most critical factor in predicting future patterns because the pious tend to reproduce more rapidly than the seculars. But the actual trends measured by the World Values Survey and by REDC indicate that religiosity is declining in most of the world. That’s because casual conversion from theism to secularism is trumping reproduction, and that in turn is because the global middle class is on the rise, leading to mass organic conversion away from religion—note that religion is not a big problem in South America or most of eastern Asia because secularism is waxing in those regions.

So why has a portion of modern religion become so venomous? In part, it’s a classic counter reaction to the success of secularization. But as troublesome as they often are, such reactionary movements tend to be temporary—remember how at the turn of the century gay-bashing was a major sociopolitical tool of the American right? Toxic theism is a symptom of a power hungry world.

Clarke may well have been right that fusion power production would have helped produce a much better 21st century world. Where he was way overoptimistic was in thinking that fusion reactors would be up and running decades ago. Clarke lived long enough to be distressed when his power dream was not coming to pass, and the unpleasant social consequences were becoming all too clear. Whether efficient hydrogen fusing plants can be made practical in the near future is very open to question, and even if they can we will have had to put up with decades of brutal strife fueled by too much religion.

That’s big news. But the even more important news that hardly any know is that modernity is winning as theism retracts in the face of the prosperity made possible by modern science and technology.

We know that emotions can influence individual well-being. Indeed, scientific progress has unveiled how human emotions—from exuberance to sorrow and even compassion—can optimize as well as hinder individual-level health outcomes. Across numerous studies we see that the intensity and flexibility of our emotions has robust effects on a wide range of cross-sectional and longitudinal well-being outcomes for the individual person. Furthermore, an optimal diversity of (both positive and negative) emotional experiences in everyday life promotes greater subjective well-being and decreased psychopathology symptoms. But are the effects of emotion on well-being specific to these types of individual-level outcomes?

Recent scientific news suggests the answer is no: Emotions (also) influence environmental well-being outcomes. These recent discoveries highlight that psychological processes, including our own emotional states, can play an important but previously understudied role in addressing pressing environmental issues. For example, exposure to scenes of environmental destruction, but not pristine landscapes, engages distinct neural regions (e.g., anterior insula) associated with anticipating negative emotions that, in turn, predicted individuals’ donations to protect national parks. Thus, negative rather than positive emotional responses may drive pro-environmental behaviors (as suggested by powerful work conducted by Brian Knutson and Nik Sawe). Such findings are critical on the heel of recent task force reports underscoring the necessity of an affective level of analysis given the collective impact of emotion relevant processes (such as emotion regulation and responding) on shaping broad-based environmental outcomes. Importantly too, are related advances from psychology providing recommendations for applying insights about individual affective reactions to increase public engagement in motivating pro-environmental behaviors.

This recently burgeoning area of work at the intersection of affective science and environmental psychology provides initial proof-of-concept demonstrations that emotions can improve environmental health by shaping our emotional reactions towards environmental issues as well as the frequency and degree of conservationist behaviors. Yet much work remains to be done, and many scientific highlights are still on the horizon. These include mapping the reciprocal relationship between our emotions and environmental choices in everyday decision-making and policy planning. We also need to learn more about how rapid changes in immediate environmental surroundings (e.g., access to clean water, local air pollution) might have reciprocal downstream effects on affective states and motivated behaviors. In addition, it will be critical to further investigate whether and how individual judgments and real-world choice behaviors can scale to aggregate policy level as well.

As pressing environmental concerns grow—including rapid deforestation, increasing carbon dioxide emissions, habitat destruction, and threats to critical areas of biodiversity—insights from affective science will become increasingly important. Thus, what appears at first glimpse as an interesting recent news topic has quickly transformed into an indefinitely critical and deeply urgent scientific focus. The time has come for social scientists to join the ranks of engineers, natural scientists, and policy makers seeking to preserve and enhance environmental well-being.

It is discomfiting for many to contemplate this fact. To assuage our minds, we imagine a nice thick wall between Us, the "Well," and Them, the "Mad."

In one episode of The Simpsons, Homer is psychiatrically hospitalized by mistake. His hand is stamped "Insane." When his psychiatrists come to believe he is not and release him, they stamp his hand "Not Insane." But sanity is not binary; it is a spectrum on which we all lie. Overt madness might be hard to miss, but what is its opposite? There is clear evidence that large numbers of people who have no psychiatric diagnosis and are not in need of psychiatric treatment experience symptoms of psychosis, notably hallucinations and delusions.

A study published this year in JAMA Psychiatry surveyed over 30,000 adults from nineteen countries and found that 5 percent of them had heard voices at least once in their life. The majority of these people never developed "full-blown" psychosis of the type observed in a person with, say, schizophrenia. An older study reported that a full 17 percent of the general "non-clinical" population had experienced psychosis at some point.

It gets even more slippery when taking into account that it isn’t always clear if an experience is psychotic or not. Why is it that someone who believes that the US government is aware of alien abductions of Americans is not deemed delusional, or merely a conspiracy theorist, and yet someone who believes that he himself has been abducted by aliens likely will be considered delusional?

The psychosis continuum is not simply a fascinating concept; it has important clinical ramifications. Unfortunately, that is news to many mental health practitioners. It is easy to see the neurobiological parallels between antidepressant medication improving mood, anxiolytic medication reducing panic, and antipsychotic medication ameliorating hallucinations. But ask a psychiatrist about providing psychotherapy to people suffering from these symptoms and, again, the wall comes up. At least here, in New York City, many people with depression and anxiety seek relief in therapy. Very few of those with psychosis are afforded its benefits, despite the fact that therapy works in treating psychotic symptoms. And here is where the lede has been buried.

Cognitive behavioral therapy (CBT)—one of the most practiced forms of therapy—while commonly applied to mood, anxiety, and a host of other psychiatric disorders, also works with psychosis. This might seem to be inherently contradictory. By definition, a delusion is held tenaciously, despite evidence to the contrary. You aren’t supposed to be able to talk someone out of a delusion. If you could, it wouldn’t be a delusion, right? Surprisingly, this is not the case.

And here we return to our thin line. Early on in CBTp, the therapist "normalizes" the psychotic experiences of the patient—perhaps going so far as to offer his own strange experiences—thereby reducing stigma and forging a strong therapeutic bond with the patient, who is encouraged to see himself not as "less than" his doctor, but further along the spectrum (the continuum model). The patient is then educated as to how stressors like child abuse or cannabis use can interact with preexisting genetic risk factors and is encouraged to reflect on the impact his life experiences might have on his symptoms (the vulnerability-stress model). Finally, the therapist reviews an Activating event, the patient’s Belief about that event, and the Consequences of holding that belief (ABC model). Over time the clinician gently challenges it and, ultimately, patient and doctor together reevaluate the belief. CBTp can be applied to hallucinations as well as to delusions.

CBTp has about the same therapeutic benefit as the older antipsychotic medication chlorpromazine (Thorazine) and the newer antipsychotic olanzapine (Zyprexa). This does not mean, of course, that people shouldn’t take antipsychotic medication when appropriate. They certainly should. The reality, however, is that many do not, and it’s not hard to understand why. These medications, while often life-saving (for the record, I have prescribed antipsychotics thousands of times), unfortunately often have adverse effects. Impaired insight (the ability to reflect on one’s inner experiences and to recognize that one is ill) is also a significant impediment to medication adherence.

Here, CBTp can have several ancillary benefits. First, the therapy can improve insight and thereby adherence. Second, if a patient refuses to take medication but is willing to engage in CBTp, he is going to do better than with no treatment at all. Finally, people receiving CBTp might ultimately require lower doses of antipsychotic medication, diminishing its toxicity and, again, increasing adherence.

The utility of CBTp shouldn’t be news, as evidence of its efficacy has been replicated over and again, but it remains so, sadly even in the mental health community, especially in the United States. While CBTp is a first-line treatment for psychosis in the UK, you would be hard-pressed to find a psychiatrist in the US who could describe how it is practiced. Good luck finding a mental health practitioner who is trained to do it. But the good news is that the news is spreading. However slowly, more clinicians are being made aware, are being trained, and are practicing CBTp. More practitioners will become available to more patients who will then receive better care (optimally, along with other well-established, psychosocial interventions like family therapy and supported employment), and we will see improved medical outcomes.

If this news sticks—and I think it will—it will have a great humanizing effect in the way society views people suffering from psychosis. After all, while there are psychotic aspects in all of our minds, it is assuredly just as true that there are healthy parts of even the most stricken of minds.

The Cartesian wall between mind and brain has fallen. Its disintegration has been aided by the emergence of a wealth of new techniques in collecting and analyzing neurobiological data, including neuroprediction, which is the use of human brain imaging data to predict how the brain’s owner will feel or behave in the future. The reality of neuroprediction requires accepting the fact that human thoughts and choices are a reflection of basic biological processes. It also has the potential to transform fields like mental health and criminal justice.

In mental health, the potential for advances in identifying and treating psychopathology has been limited by existing diagnostic practices. In other fields of medicine, new diagnostic techniques like genetic sequencing have led to more targeted treatments for tumors and pathogens and major improvements in patient outcomes. But mental disorders are still diagnosed as they have been for a hundred years: using a checklist of symptoms derived from a patient’s subjective reports or a clinician’s subjective observations. This is like trying to determine whether someone has leukemia or the flu based on subjective evaluations of weakness, fatigue, and fever. The checklist approach not only makes it difficult for a mental health practitioner to determine what afflicts a patient—particularly if he is unwilling, unmotivated, or unable to report his symptoms—it provides no information about what therapeutic approach will be most effective.

In criminal justice, parallel problems persist in sentencing and probation. Making appropriate sentencing and probation decisions is hampered by the difficulty of determining whether a given offender is likely to reoffend after being released—decisions that are also based on largely subjective criteria. As a result, those who likely would not recidivate are often detained for too long, and those who will recidivate are released—both suboptimal outcomes.

Neuroprediction may yield solutions to these problems. One recent study found that the relative efficacy of different treatments for depression could be predicted from a brain scan that measured metabolic activity in the insula. Another found that predictions about whether paroled offenders would recidivate were improved using a brain scan that measured hemodynamic activity in the anterior cingulate cortex. Neither approach is ready for widespread use yet, in part because predictive accuracy at the individual level is still only moderate, but inevitably they—or improvements upon them—will be.

This would be an enormous advance in mental health. Presently, treatment outcomes for disorders like depression remain stubbornly poor; up to 40 percent of depressed patients fail to respond to the first-line treatment, the selection of which still relies more or less on guesswork. Using neuroprediction to improve this statistic could dramatically reduce suffering. Because required brain scans are expensive and their availability limited, however, disparities in access would be a concern.

Neuroprediction of crime presents quite a different scenario, as its primary purpose would be to improve outcomes for society (less crime, fewer resources spent on needless detentions) rather than for the potential offender in question. It is difficult to imagine this becoming accepted practice without concomitant changes in our approach to criminal behavior, namely, shifting the focus away from retribution and toward rehabilitation. In furthering understanding of the biological basis of persistent offending, neuroprediction may actually help in this regard.

Regardless, neuroprediction, at least the beta version of it, is here. Now is the time to consider how to harness its potential.

For fifteen years, popular-science readers have gotten used to breathless claims about commercial quantum computers just around the corner. As far as I can tell, though, 2015 marked a turning point. For the first time, the most hard-nosed experimentalists are talking about integrating 40 or more high-quality quantum bits (“qubits”) into a small programmable quantum computer—not in the remote future, but in the next few years. If built, such a device will probably still be too small to do anything useful, but I honestly don’t care.

The point is, forty qubits are enough to do something that computer scientists are pretty sure would take trillions of steps to simulate using today’s computers. They’ll suffice to disprove the skeptics, to show that nature really does put this immense computing power at our disposal—just as the physics textbooks implicitly predicted since the late 1920s. (And if quantum computing turns out not be possible, for some deep reason? To me that’s unlikely, but even more exciting, since it would mean a revolution in physics.)

So then, is imminent quantum supremacy the “most interesting recent [scientific] news”? I can’t say that with any confidence. The trouble is, which news we find interesting depends on how widely we draw the circle about our own hobbyhorses. And some days, quantum computing seems to me to fade into irrelevance next to the precarious state of the earth. Perhaps when people look back a century from now, they’ll say that the most important science news of 2015 was that the West Antarctic Ice Sheet was found to be closer to collapse than even the alarmists predicted. Or, just possibly, they’ll say the most important news was that in 2015, the “AI risk” movement finally went mainstream.

This movement posits that superhuman artificial intelligence is likely to be built within the next century, and that the biggest problem facing humanity today is to ensure that, when the AI arrives, it will be “friendly” to human values (rather, than, say, razing the solar system for more computing power to serve its inscrutable ends). I like to tease my AI-risk friends that I’ll be more worried about the impending AI singularity when my Wi-Fi stays working for more than a week. But who knows? At least this scenario, if it panned out, would render the melting glaciers pretty much irrelevant.

Instead of expanding my “circle of interest” to encompass the future of civilization, I could also contract it more tightly, around my fellow theoretical computer scientists. In that case, 2015 was the year that Lazslo Babai of the University of Chicago announced the first “provably fast” algorithm for one of the central problems in computing: graph isomorphism. This problem is to determine whether two networks of nodes and links are “isomorphic” (that is, whether they become the same if you relabel the nodes). For networks with N nodes, the best previous algorithm—which Babai also helped to discover, thirty years ago—took a number of steps that grew exponentially with the square root of N.

The new algorithm takes a number of steps that grows exponentially with a power of log(N) (a rate that’s called “quasi-polynomial”). Babai’s breakthrough probably has no applications, since the existing algorithms were already perfectly fast for any networks that would ever arise in practice. But for those who are motivated by an unquenchable thirst to know the ultimate limits of computation, this is arguably the biggest news so far of the twenty-first century.

Drawing the circle even more tightly, in “quantum query complexity”—a tiny subfield of quantum computing that I cut my teeth on as a student—it was discovered this year that there are Boolean functions that a quantum computer can evaluate in less than the square root of the number of input accesses that a classical computer needs, a gap that had stood as the record since 1996. Even if useful quantum computers are built, this result will have zero applications, since the functions that achieve this separation are artificial monstrosities, constructed only to prove the point. But it excited me: it told me that progress is possible, that the seemingly-eternal puzzles that drew me into research as a teenager do occasionally get solved. So damned if I’m not going to tell you about it.

At a time when the glaciers are melting, how can I justify getting excited about a new type of computer that will be faster for certain specific problems—let alone about an artificial function for which the new type of computer gives you a slightly bigger advantage? The “obvious” answer is that basic research could give us new tools with which to tackle the woes of civilization, as it’s done many times before. Indeed, we don’t need to go as far as an AI singularity to imagine how.

By letting us simulate quantum physics and chemistry, quantum computers might spark a renaissance in materials science, and allow (for example) the design of higher-efficiency solar panels. For me, though, the point goes beyond that, and has to do with the dignity of the human race. If, in millions of years, aliens come across the ruins of our civilization and dig up our digital archives, I’d like them to know that before humans killed ourselves off, we at least managed to figure out that the graph isomorphism problem is solvable in quasi-polynomial time, and that there exist Boolean functions with super-quadratic quantum speedups. So I’m glad to say that they will know these things, and that now you do too.

Physicists have spent the last 100 years attempting to reconcile Einstein’s theory of general relativity, which describes the large-scale geometry of spacetime, with quantum mechanics, which deals with the small-scale behavior of particles. It’s been slow going for a century, but now, suddenly, things are happening.

First, there’s ER=EPR. More idea than equation, it’s the brainchild of physicists Juan Maldacena and Leonard Susskind. On the left-hand side is an Einstein-Rosen bridge, a kind of geometric tunnel connecting distant points in space, otherwise known as a wormhole. On the right are Einstein, Rosen, and Podolsky, the three physicists who first pointed out the spooky nature of quantum entanglement, wherein the quantum state of two remote particles straddles the distance between them. Then there’s that equals sign in the middle. Boldly it declares: spacetime geometry and the links between entangled particles are two descriptions of the same physical situation. ER = EPR appears brief and unassuming, but it’s a daring step toward uniting general relativity and quantum mechanics—with radical implications.

Intuitively, the connection is clear. Both wormholes and entanglement flout the constraints of space. They’re shortcuts. One can enter a wormhole on one side of the universe and emerge from it on the other without having to traverse the space in between. Likewise, measuring one particle will instantaneously determine the state of its entangled partner, even if the two are separated by galaxies.

The connection becomes more intriguing when viewed in terms of information. For maximally entangled particles, the information they carry resides simultaneously in both particles but in neither alone; informationally speaking, no space separates one from the other. For particles that are slightly less than maximally entangled, we might say there is some space between them. As particles become less and less entangled, information becomes more and more localized, words like “here” and “there” begin to apply and ordinary space emerges.

One hundred years ago, Einstein gave us a new way to think about space—not as the static backdrop of the world, but as a dynamic ingredient. Now, ER=EPR gives us yet a newer interpretation: what we call space is nothing more than a way to keep track of quantum information. And what about time? Time, physicists are beginning to suspect, may be a barometer of computational complexity.

Computational complexity measures how difficult it is to carry out a given computation—how many logical steps it takes, or how the resources needed to solve a problem scale with its size. Historically, it’s not something physicists thought much about. Computational complexity was a matter of engineering—nothing profound. But all that has changed, thanks to what’s known as the black hole firewall paradox—an infuriating dilemma that has theoretical physicists pulling out their hair.

As a black hole radiates away its mass, all the information that ever fell in must emanate back out into the universe, scrambled amongst the Hawking radiation; if it doesn’t, quantum mechanics is violated. The very same information must reside deep in the black hole’s interior; if it doesn’t, general relativity is violated. And the laws of physics decree: information can’t be duplicated. The firewall paradox arises when we consider an observer, Alice, who decodes the information scrambled amongst the Hawking radiation, then jumps into the black hole where she will find, by various accounts, an illegal information clone or an inexplicable wall of fire. Either way, it’s not good.

But Alice’s fate recently took a turn when two physicists, Patrick Hayden and Daniel Harlow, wondered how long it would take her to decode the information in the radiation. Applying a computational complexity analysis, they discovered that the decoding time would rise exponentially with each additional particle of radiation. In other words, by the time Alice decodes the information, the black hole will have long ago evaporated and vanished, taking any firewalls or violations of physics with it. Computational complexity allows general relativity and quantum mechanics to peacefully coexist.

Hayden and Harlow’s work connects physics and computer science in a totally unprecedented way. Physicists have long speculated that information plays a fundamental role in physics. It’s an idea that dates back to Konrad Zuse, the German engineer who built the world’s first programmable electronic computer in his parent’s living room in 1938, and the first universal Turing machine three years later. In 1967, Zuse wrote a book called Calculating Space, in which he argued that the universe itself is a giant digital computer. In the 1970s, the physicist John Wheeler began advocating for “it from bit”—the notion that the physical, material world is, at bottom, made of information. Wheeler’s influence drove the burgeoning field of quantum information theory and led to quantum computing, cryptography and teleportation. But the idea that computational complexity might not only describe the laws of physics, but actually uphold the laws of physics, is entirely new.

It’s odd, on glance, that something as practical as resource constraints could tell us anything deep about the nature of reality. And yet, in quantum mechanics and relativity, such seemingly practical issues turn out to be equally fundamental. Einstein deduced the nature of spacetime by placing constraints on what an observer can see. Noticing that we can’t measure simultaneity at a distance gave him the theory of special relativity; realizing that we can’t tell the difference between acceleration and gravity gave him the curvature of spacetime. Likewise, when the founders of quantum mechanics realized that it is impossible to accurately measure position and momentum, or time and energy, simultaneously, the strange features of the quantum world came to light. That such constraints were at the heart of both theories led thinkers such Arthur Stanley Eddington to suggest that at its deepest roots, physics is epistemology. The new computational complexity results push further in that direction.

So that’s the news: a profound connection between information, computational complexity and spacetime geometry has been uncovered. It’s early to say where these clues will lead, but it’s clear now that physicists, computer scientists and philosophers will all bring something to bear to illuminate the hidden nature of reality.

The most exciting news in our scientific quest to understand the nature of culture is not a single result, but a fundamental change in the metabolism of research: With increasing availability of cultural data, more and more robust quantification nurtures further qualitative insight; taken together, the results inspire novel conceptual and mathematical models, which in turn put into question and allow for accelerated modification of existing data models; Closing the loop, better models lead to more efficient collection of even more cultural data. In short, the hermeneutic circle is replaced by a hermeneutic hypercycle. Driven by the quantification of non-intuitive dynamics, cultural science is accelerated in an auto-catalytic manner.

The original "hermeneutic circle" characterizes the iterative research process of the individual humanist to understand a text or an artwork. The circle of hermeneutic interpretation arises as understanding specific observations presupposes an understanding of the underlying worldview, while understanding the worldview presupposes an understanding of specific observations. As such, the hermeneutic circle is a philosophical concept that functions as a core principle of individual research in the arts and humanities. Friedrich Ast explained it implicitly in 1808, while Heidegger and Gadamer further clarified it in the mid-20th century.

Unfortunately the advent of large database projects in the arts and humanities has almost disconnected the hermeneutic circle in practice. Over decades, database models, to embody the underlying worldview, were mostly established using formal logic and a priori expert intuition. Database curators were subsequently used to collect vast numbers of specific observations, enabling further traditional research, while failing to feed back systematic updates into the underlying database models.

As a consequence, "conceptual reference models" are frozen, sometimes as ISO standards, and out of sync with the non-intuitive complex patterns that would emerge from large numbers of specific observations by quantitative measurement. A systematic data science of art and culture is now closing the loop using quantification, computation, and visualization in addition.

The "hermeneutic hypercycle" is a term that returned no result in search engines before this contribution went online. A product of horizontal meme-transfer, it combines the hermeneutic circle with the concept of the catalytic hypercycle, as introduced by Eigen and Schuster. Like the carbon-cycle that keeps our sun shining and the citric acid cycle that generates energy in our cells, the hermeneutic circle in data-driven cultural analysis can be understood as a cycle of "reactions", here to nurture our understanding of art and culture.

The cycle of reactions is a catalytic hypercycle, as data collection, quantification, interpretation, and data modeling all feed back to catalyze themselves. Their cyclical connection provides a mutual corrective of bias (avoiding an error catastrophe) and leads to a vigorous growth of the field (as we learn what to learn next). In simple words, data collection leads to more data collection, quantification leads to more quantification, interpretation leads to more interpretation, and modeling leads to more modeling. Altogether, data collection nurtures quantification and interpretation, which in turn nurtures modeling, which again nurtures data collection, etc.

It is fascinating to observe the resulting vigorous growth of cultural research. While the naming game of competing terms such as digital humanities, culture analytics, culturomics, or cultural data science is still going on, it becomes ever more clear that we are on our way to a sort of systems biology of cultural interaction, cultural pathways, and cultural dynamics, "Broadly" defined. The resulting "systematic science of the nature of culture" is exciting news as most issues from religious fundamentalism to climate change require cultural solutions and "nature cannot be fooled".

Sterility is not considered healthy anymore. Medicine is shifting from an antibiotic towards a probiotic approach and the idea of hygiene is becoming an organization of contamination rather, as opposed to disinfection. Last year, it was determined that the placenta is not sterile after all. The growing fetus was earlier believed to thrive in an absolutely clean bubble; instead, it seems to be confronted with germs through the filter of its mother’s biological system and building its future immune system from the very start of cell division.

There are trillions of viruses, bacteria, fungi and parasites thriving in each of us right now. Around two pounds of our body weight consists of what are popularly called bugs. Many of the microbiotic organisms are ancient. The most feared are viruses like Ebola or HIV, bacteria like streptoccocus, and parasites like rabies. But next to the few fast and furious, scary exceptions, most of the common organisms are rather easy to deal with for our immune system—even when they are pathogenic. New research suggests that many of them are actually keeping us healthy; they seem to be “training” our immune system.

The term “microbiome” was coined in the 1990’s, but research is still in the beginnings of sorting out the good from the bad. As this community of organisms is so manifold and complex, one speaks about a sea, a forest, a new natural world to be discovered within us. The main idea so far is, that the more diversity—not just in the environment we live in, but also in the environment that lives within us—the better.

One rather simple clinical treatment that has turned into a substantial new industry is fecal transplants from healthy to sick people. Not just has it shown to heal the colon from an overgrowth of clostridium difficile, a bacteria that often can not be cured by antibiotics anymore, but in the way it helped obese people miraculously lose weight. And as it turns out the gut is fundamentally intertwined with our brain and it influences our psychological sanity.

Current research points to how certain bacterial cultures cause anxiety, depression, and even Alzheimers, while others might be able to help alleviate these ailments. But the impact on our state of mind seems to be even more shockingly direct if we take toxoplasmosis as an example: a neuro-active parasite that influences one of the most existential feelings, sexual attraction.

We, alongside mice and other mammals, are only intermediary hosts—cats are its main target. In this unconscious ménage a trois the parasite wants the mouse to be attracted to the cat, so it travels up to the region of the mouse’s brain where sexual arousal occurs, and there it lets that mouse react to the scent of the pheromones of the cat. This again makes the mouse dizzy and lets it approach the cat instead of fleeing, so that the cat can much more easily catch it and ingest it.

Once inside that cat, the parasite has reached its goal, it can reproduce. Humans are part of its scheme in more abstract ways. Those who carry it are more attracted to scents that originate from cat pheromones. This scent can be found in many perfumes—allegedly Chanel No. 5 for instance. Overall, about thirty percent of the global population are infected—quite a target group! Apparently, this segment of humanity is also more prone to be involved in car accidents, and female carriers are known to acquire more designer clothes.

We tend to see sexuality as one of the main markers of our individuality, but not only does our own biological system react to sexual attractions in ways that we can’t control, there are also parasites that can neurologically influence, or possibly even direct our behaviour. It’s a provocative and difficult topic and it challenges the fundamental understanding of what it means to be human.

We are in constant exchange with our germs. We shake hands, kiss, have sex, travel, use toilets, go to parties, churches… Why do we do that? Touching the same wall, drinking from the same cup. Now there is research about how religion, as a social event, might be entangled in that complicated communal sharing of microbial organisms. When we come together, what do we really exchange? Might it be that our need for social interaction is also influenced by the secret powers of microbes?!

When fear and tension rise across racial and ethnic divisions, as they have in recent years, genetic arguments to explain behavioral differences can quickly become popular. However, we know racial and ethnic groups may also be exposed to vastly different experiences, also likely to strongly impact behavior. Despite seemingly inexhaustible interest in the nature/nurture debate, we are only starting to learn how the interaction of genes with experience may alter the potential of individuals, and to see how individual decision making styles can alter the potential wealth of nations.

A captivating news image I saw this year depicted two sets of identical twins, mixed up as infants, raised in separate families in Colombia. The men and their families assumed they were fraternal twins, who share genes only as much as siblings and therefore do not look alike. Only in adulthood, did the young men discover the mistake and find their identical twin brothers through the recognition of friends. One mixed pair of twins grew up in the city and the other in countryside with far more modest resources. We are all, of course, eager to know how these different environments altered these men’s personalities, preferences, intelligence, and decision making when their genes were the same. We are all probably fairly comfortable with the idea that trauma, hardship, or parenting style could impact our emotional development and our emotional patterns even in adulthood. However, it is less clear how early experience might affect how we think and make decisions. Is one identical twin more likely to save his money, repeat a mistake, take a short cut, buy lottery tickets, or stubbornly resist changing his mind because he was raised in a different situation? Or would the identical twin pair make the same choice regardless of upbringing? The answers from these twins are still emerging and the sample is, of course, anecdotally small. If we definitively knew the answer to these questions we might change how we view parenting and investment in childcare and education.

A growing body of work now effectively models this “twins raised apart” situation in genetically identical strains of inbred mice or genetically similar rats. Rodents get us away from our cultural biases and can be raised in conditions that model human experience of adversity and scarcity in infancy and childhood. In one early life stress model, the mother rodent is not given adequate nesting material and moves about the cage restlessly, presumably in search of more bedding. In other models, the rodent pups are separated from the mother for portions of the day or are housed alone after weaning. These offspring are then compared to offspring that have been housed with adequate nesting material and have not been separated more than briefly from their mother or siblings.

Investigators first focused early life adversity research in rodent models on emotional behavior. This research found that early life adversity increased adult stress and anxiety-like behavior. More recent studies, including some from my own lab, find that early life adversity can also impact how rodents think—the way they solve problems and make decisions. Rats and mice that have been subjected to greater early life stress tend to be less cognitively flexible (stubbornly applying old rules in a laboratory task after new rules are introduced) and they may also be more repetitive or forgetful. Some of the differences in behavior have been found to fade as the animals get older but others grow stronger and persist into adulthood.

I would hesitate to say that one group was smarter, since it is hard to determine what would be an optimal style for a wild rodent. We might see stubborn, inflexible or repetitive behavior as unintelligent in a laboratory test. In some real world situations, the same behavior might become admirable as “grit” or perseverance.

Competing theories attempt to explain these changes in emotional behavior, problem solving, and decision making. The brains of the mice that experienced adversity may be dysfunctional, in line with evidence of atrophy of frontal neurons after stress. Alternatively, humans and rodents may show positive adaptation to adversity. In one model currently growing in popularity, a brain developing under adversity may adopt a “live fast-die young” strategy that favors earlier maturation and short-term decision making. In this adaptive calibration model, animals that are genetically identical might express different set of genes in their brain and develop different neural circuits in an attempt to prepare their brain for success in the kind of environment in which they find themselves. It is unclear how many different possible trajectories might be available or when or how young brains are integrating environmental information. However, based on this adaptive calibration model, in lean times versus fat times you might expect a single species to “wire-up” different brains and display different behaviors without requiring genetic change or genetic selection at the germline level (level of eggs and sperm).

Why should we care? This data might explain population level economic behavior and offer a powerful counter narrative to seductive genetic explanations of success. Another piece of captivating news out this year was Nicholas Wade’s review of Garret Jones’ Hive Mind in the Wall Street Journal. Reading this review, I learned that national savings rates correlate with average IQ scores even if individual IQ scores do not; the subtitle is “How your nation’s IQ matters so much more than your own.” In his review, Wade (not Jones) suggests we need to look to “evolutionary forces” to explain IQ and its correlated behavioral differences. The well controlled data from experiments in mice and rats suggest we also look to early life experience. Rodents are not known for their high IQ, but the bottom line is what intelligence they have is sensitive to early life experience, even when we hold germline genetics constant. Putting these rodent and human findings together, one might hypothesize that humans exposed to instability or scarcity in early life are developing brains that are wired for shorter term investment and less saving. Thus, rather than dismissing the successes and failures of nations to slow changing genetic inheritance, we might foster a brighter future by paying more attention to the quality of early life experiences.

The neural network has been resurrected. After a troubled sixty-year history, it has crept into the daily lives of hundreds of millions of people, in the span of just three years.

In May, 2015, Sundar Pichai announced that Google had reduced errors in speech recognition to 8 percent, from 23 percent only two years earlier. The key? Neural networks, rebranded as "deep learning." Google reported dramatic improvements in image recognition just six months after acquiring DNN Research, a startup founded by Geoffrey Hinton and two of his students. Backpropagation is back—with a big data bang. And it’s suddenly worth a fortune.

The news wasn’t on the front pages. There was no scientific breakthrough. Nor was there a novel application.

Why is it news? The scale of the impact is astonishing, as is the pace at which it was achieved. Making sense of noisy, infinitely variable, visual and auditory patterns has been a holy grail of artificial intelligence. Raw computing power has caught up with decades-old algorithms. In just a few short years, the technology has leapt from laboratory simulations of oversimplified problems to cell phone apps for the recognition of speech and images in the real world.

Theoretical developments in neural networks have been mostly incremental since the pioneering work on self-organization in the 1970s and backpropagation in the 1980s. The tipping point was reached recently not by fundamentally new insights, but by processing speeds that make possible larger networks, bigger datasets, and more iterations.

This is the second resurrection of neural networks. The first was the discovery by Geoffrey Hinton and Yann LeCun that multilayered networks can learn nonlinear classification. Before this breakthrough, Marvin Minsky had all but decimated the field with his publication in 1969, Perceptrons. Among other things, he proved that Frank Rosenblatt’s perceptron could not learn classifications that are nonlinear.

Rosenblatt developed the perceptron in the 1950s. He built on foundational work in the 1940 by McCulloch and Pitts, who showed how patterns could be handled by networks of neurons, and Donald Hebb, who hypothesized that the connection between neurons is strengthened when connected neurons are active. The buzz created by the perceptron can be relived by reading "Electronic ‘Brain’ Teaches Itself," published by the New York Times on July 13, 1958. The Times quoted Rosenblatt saying that Perceptron "will grow wiser as it gains experience," adding that "the Navy said it would use the principle to build the first Perceptron ‘thinking machines’ that will be able to read or write."

Minsky’s critique was a major setback, if not a fatal one, for Rosenblatt and neural networks. But a few people persisted quietly, among them Stephen Grossberg, who began working on these problems while an undergraduate at Dartmouth in the 1950s. By the 1970s, Grossberg had developed an unsupervised, (self-organizing) learning algorithm that balanced the stability of acquired categories with the plasticity necessary to learn new ones.

Hinton and LeCun addressed Minsky’s challenge and brought neural nets back from obscurity. The excitement about backpropagation drew attention to Grossberg’s model, as well as to the models of Fukushima and Kohonen. But in 1988, Steven Pinker and Alan Prince did to neural nets what Minsky did two decades earlier, with a withering attack on their worthiness for explaining the acquisition of language. Neural networks faded into the background again.

After Geoffrey Hinton and his students won the ImageNet challenge in 2012, with a quantum improvement in performance on image recognition, Google seized the moment, and neural networks came alive again.

The opposition to deep learning is gearing up already. All methods benefit from powerful computing, and traditional symbolic approaches also have demonstrated gains. Time will tell which approaches prevail, and for what problems. Regardless, 2012-2015 will have been the time when neural networks placed artificial intelligence at our fingertips.

On October 4, 1957, the Soviet Union launched Sputnik, the first artificial satellite to this planet. That was very big news, the beginning of an era of space exploration involving multiple launchings and satellites spending long periods in orbit, launched from many nations. In the weeks after Sputnik, however, another news story played out that led to a range of other actions based on the recognition that US education was falling behind, not only in science but it other fields of education as well, such as geography and foreign languages. This is still true. We are not behind at the cutting edge, but we are behind in general broad-based understanding of science, and this is not tolerable for a democracy in an increasingly technological world.

The most significant example is climate change. It turns out, for instance, that many basic terms are unintelligible to newspaper readers. Recently I encountered the statement that "a theory is just a guess—and that includes evolution"—not to mention most of what has been reconstructed by cosmologists about the formation of the universe. When new data is published that involves a correction or expansion of earlier work, this is taken to indicate weakness, rather than the great strength of scientific work as an open system, always subject to correction by new information. When the winter temperature dips below freezing, you hear, "This proves that the earth is not warming." Most Americans are not clear on the difference between "weather" and "climate." The United States government supports the world’s most advanced research on climate but the funds to do so are held hostage by politicians convinced that it is a hoax. And we can add trickle down economics and theories of racial and gender inferiority to the list of popular prejudices that many Americans believe are ratified by science, not to mention the common conclusion that the "War on Poverty failed." Why do we believe that violence is a solution?

Among the popular misconceptions of scientific concepts is a totally skewed concept of "cybernetics" as dealing only with computers. It is true that key concepts developed in the field of cybernetics resulted in computers as an extremely important by-product, but the more significant achievement of cybernetics was a new way of thinking about causation, now more generally referred to as systems theory. Listen to the speeches of politicians proclaiming their intent to solve problems like terrorism—it’s like asking for a single pill that will "cure" old age—if you don’t like x, you look for an action that will eliminate it, without regard to the side effects (bombing ISIL increases hostility for example) or the effects on the user (consider torture). Decisions made with overly simple models of cause and effect are both dangerous and unethical.

The news that has stayed news is that American teaching of science is still in trouble, and that errors of grave significance are made based on overly simple ideas of cause and effect, all too often exploited and amplified by politicians.

When I worked as a journalist at the science desk of the New York Times, our editors were constantly asking us to propose story ideas that were new, important, and compelling. The potential topics in science news are countless, from genetics to quantum physics. But if I were at the Times today, I’d pitch three science stories, all of which are currently under the collective radar, and each of which continue to unfold and will have continued—and mounting—significance for our lives in years ahead.

For one: Epigenetics. Once the human genome was mapped, the next step has been figuring out how it works, including what turns on and off all those bits of genetic code. Here, everything from our metabolism, to our diet, to our environment and what habits we learn comes into play. A developing story here will be neuroplasticity, a case in point of epigenetics. First considered seriously a decade or so ago, neuroplasticity—the brain’s constant reshaping through repeated experiences—presents a potential for neural hacking apps. As neuroscientists like Judd Brewer at Yale and Richard Davidson at the University of Wisconsin have shown, we can choose which elements of brain function we want to strengthen through sustained mind training. Do you want to have better emotion regulation, enhance your concentration and memory, or become more compassionate? Each of these goals means strengthening distinct neural circuitry through specific, bespoke mental exercise, which might one day become a new kind of daily fitness routine.

The second: industrial ecology as a technological fix. This new discipline integrates fields ranging from physics and biochemistry to environmental science with industrial design and engineering to create a new method—life cycle assessment (or LCA)—for measuring the ecological costs of our material world. LCA gives a hard metric for how something as ubiquitous as a mobile phone impacts the environment and public health at every stage in its life cycle. This methodology gives us a fine-grained lens on how human activities degrade the global systems that support life, and points to the specific changes that would bring most benefit. Some companies now are using LCA to change the ways their products are made so that they replenish rather than deplete. As work at the Harvard School of Public Health illustrates, this means using LCA to shift away from the footprint metric, how much damage we do to the planet, to the handprint, measuring the good we do—how much we reduce our footprint. A news peg: companies are about to release the first major "net-positive products," which, analyzing their entire life cycle, replenish rather than deplete.

Finally: the inverse relationship between power and social awareness, which integrates psychology into political science and sociology. Ongoing research at the University of California at Berkeley by psychologist Dacher Keltner—and at other research centers around the world—shows that people who are higher in social power, whether through wealth, status, rank or the like, pay less attention in a face-to-face encounter to those who hold less power. Little attention means little empathy or understanding. The resulting empathy deficit means that those who wield power, such as wealthy politicians, have virtually no sense of how their decisions impact those with little power. Movements like Occupy, Black Lives Matter, and the failed Arab Spring can be read as attempts to heal this divide. This empathy deficit will drive political tensions far into the future. Unless, perhaps, those in power follow Gandhi’s dictate to consider how their decisions affect "the poorest of the poor."

In one hand you’re holding a gallon of gasoline weighing six pounds, in the other a three pound battery, now imagine them containing equal energy. Spoiler alert: they already can. The most exciting and far-reaching scientific advance is the dramatic increase in electric battery density allowing it to displace gasoline, and solving the problems of night electricity, vehicle range, and becalmed windmills.

Electric car range extends about 9 percent every year and has reached a point where one can imagine round trips that don’t involve a flatbed. But the public was startled in 2011 when a seven figure prize was claimed from Green Flight Challenge who offered it to an aircraft that could fly 200 miles under two hours with a passenger using less than one gallon of fuel. Three planes competed—two electric and one a hybrid—with only the electric planes finishing under two hours. The winner averaged 114 mph on a plug-in electric plane sans a gas engine; this was a Tom Swift fantasy five years earlier as the weight of the batteries couldn’t be lifted by the plane even if they could be crammed into fuselage. The weight and size of the battery shrunk while its energy storage increased.

Presently, our battery density peaks at about 250 watts-hours per kilogram, up dramatically from the 150 Wh/Kg a few years ago, but still far inferior to petroleum, which is 12,000 Wh/Kg or about 30x. One company is about to release a 400 Wh/Kg, up 60 percent over the previous best, but batteries under development could pass the energy density of fossil fuels within a few years.

The most exciting and counterintuitive battery invented is the lithium-air that inhales air for the oxygen needed for its chemical reaction and exhales the air when finished. This should ring a bell given the similarity to gas engines: they inhale air, add a gas mist and the expanding air creates power, but then expels an atmospheric sewer. The lithium air battery is solid-state and exhales clean air. MIT has already demonstrated a Lithium-air battery with densities of over 10,000 Wh/kg.

Batteries need not have the energy density of gasoline in order to replace it as the physics of harnessing gasoline power are lame, only 15 percent of the energy in one’s tank motors the car down the highway. The rest is lost to heat, engine and transmission weight, friction, and idling. As a practical matter, batteries in the labs are already beyond useable energy density of fossil fuels, an energy density that results in a 500 mile range for an electric car with a modest battery, probably more for a small plane.

The second dramatic change happening now is that increased battery density has lowered both the size and cost of electrical storage, creating the bridge between intermittent wind, daytime photovoltaic energy, and the round-the-clock current demands of the consumer.

Windmills produce prodigious electricity during a good blow but bupkis when becalmed, hence the batteries provide steady current until a breeze appears. A new battery installation at Elkins, West Virginia windfarm allows the 98 megawatt turbines to be a constant part of the overall grid supply, with pollutant free electricity and the reliability of a conventional fossil fuel plant.

Also fossil fuel plants run at higher capacity than needed in case of a spike in demand, not needed with a battery backup: a new megawatt battery installation in Atacama Desert, Chile brought stability to their grid and a reduction in fuel usage.

The green revolution that has been hoped for is suddenly here, improbably due to the humble battery. A century ago there were more electric cars on the road than gasoline cars, very soon we will be back to the future.

The increasing use of drone technology is revolutionizing the ways we think about doing wildlife science and changing the kinds of things we can observe. As a marine mammal scientist who studies cetaceans, dolphins and whales, I see how drones, referred to more formally as unmanned aerial vehicles (UAV), are affording scientists extended perceptions, far less intrusive means of observing and documenting animal behavior, and new approaches towards protecting wildlife. Drones bring with them a new set of remote sensing and data collection capabilities.

The Holy Grail of observing animals in the wild is not being there, because you’re very presence is often a disturbing influence. Drones are a potential solution to this paradox. Along with the use of drones, it will be important to determine the “critical distance” for approaching specific species and individuals being wary to not encroach too closely. Surely there will be an art and science to getting it right. Imagine the feeling of exhilaration and presence soaring above a socializing pod of whales or dolphins as you spy on them from on high. We can now witness much of what was the secret life of these magnificent mammals, previously unobservable to us, and do it in a disembodied manner. We can observe myriad behaviors and nuances of interactions that could not formerly be seen due to the horizontal view afforded from a research boat, or that would have been interrupted by the very presence of an approaching research vessel.

Animal health assessments and animal rescues are being conducted by veterinarians and researchers with the aid of drones. For example, the Whalecopter, a small drone developed by research scientists at Woods Hole Oceanographic Institution in Massachusetts, took high resolution photographs of whales to document fat levels and skin lesions and then hovered in at closer range to collect samples of whale breath to study bacteria and fungi in their blow—the air whales expel from their blowhole. NOAA scientists in Alaska are using drones to help them with beluga whale strandings in Cook Inlet. Drones are a cost-effective means of getting critical and timely information about the conditions of stranded beluga whales—their location, the number and relative age of the stranded animals, whether they are submerged or partially stranded. All this information could be important for their chance for survival. The relayed images from drones are often clearer than those obtained by a traditional aerial surveys by planes. Even if they can’t save an individual whale, getting to them more quickly enables scientists to conduct a necropsy on fresher tissue and determine the cause of death that could effect the survival of other whales in the future.

Patrol drones are already being used to monitor and protect wildlife from poachers. One organization, Air Shepherd, has effectively been deploying drones in Africa to locate poachers seeking elephant ivory and rhino horns. Preprogrammed drones monitor high traffic areas where the animals are known to congregate—and known also to the poachers. They have already been effective in locating poachers and informing local authorities of their location for subsequent arrest.

It is inspiring to envision all the observations and discoveries that may be in store for us through our deployment of these remote observers, individually or in swarms. In my field, a future generation of cetacean seeking drones may be around the corner—drones programmed to find cetacean shaped forms and follow them. This is a new era of wildlife observation and monitoring. One can envision using a small fleet of “journalist drones” to monitor and provide real time video feeds on the welfare of various species, in our oceans, on our savannahs and in our jungles. We might call it Whole World Watching (WWW) and create a global awareness and a more immediate connection with the other species that share our planet.

The germ theory of disease launched a revolution that transformed medicine. For the first time in history, disease was understood as an attack by microscopic organisms, organisms that were soon identified, characterized, and defeated.

Yet, we have not conquered disease. Cancer, heart disease, diabetes, stroke, and parasitic diseases are just some of the top causes of death in the world today. Research has helped us understand and treat these illnesses, but is there a unifying principle explaining them all? a common mechanism that could help us transform biomedical science once again?

Perhaps there is: the immune system.

Since 430 BC we have known of biological structures and processes that protect the body against disease; but even today we are just beginning to understand how deeply involved they are in our lives. The immune system’s cellular sentries weave an intricate early warning network through the body; its signaling molecules—the cytokines—trigger and modulate our response to infection, including inflammation; it is involved in even so humble a process as the clotting of blood in a wound. Today we are beginning to grasp how—from cancer to diabetes, from heart disease to malaria, from dementia to depression—the immune system is involved at a fundamental level, providing us with the framework to understand, and to better treat these wide-ranging ailments.

Cancer is often described as a "wound that never heals," referring to the chronic inflammatory state that creates a tumor-promoting environment. Indeed, cancer cells develop into a tumor by disabling and hijacking components of the immune system; immunosuppression and tumor-promoting inflammation are the two facets of cancer immunology. Both Type 1 and Type 2 diabetes are linked to the immune system; the former is an autoimmune condition in which the immune system attacks the insulin-producing cells in the pancreas, while the latter is linked to insulin resistance through high levels of cytokines produced during inflammation. Pro-inflammatory cytokines are also linked to heart disease, which is the leading cause of death in the developed world. The malaria parasite is an expert at manipulating our immune system, cloaking itself in molecules that reassure our immune sentinels that nothing is amiss, while wreaking havoc in our blood cells. Most intriguingly, we are now beginning to learn about the neuroimmune system, a dense network of biochemical signals synthesized in neurons, glial cells, and immune cells in our brains critical to the function of our central nervous system. Perhaps unsurprisingly, these markers of the neuroimmune system are disrupted in disorders such as depression, anxiety, stroke, Alzheimer's disease, Parkinson's disease, and multiple sclerosis. Cytokine levels have been shown to vastly increase during depressive episodes, and—in people with bipolar disorder—to drop off in periods of remission. Even the stress of social rejection or isolation causes inflammation, leading to the fascinating idea that depression could be viewed as a physiological allergic reaction, rather than simply a psychological condition.

With this knowledge comes power: modulating the immune system to our advantage is a burgeoning field of research, particularly for cancer. Cancer immunotherapy heralds a turning point in treatment, with astonishingly rapid remissions achieved in some patients undergoing early stage clinical trials. New classes of drugs known as "checkpoint inhibitors" target specific immunological pathways, and we can reprogram "designer immune cells" to target cancer cells, changing the way that cancer is treated. Aspirin, a humble drug that reduces inflammation, may even be able to prevent some cancers; a tantalizing possibility that is currently being investigated in a large-scale clinical trial. Aspirin is already known to prevent heart attack and stroke in some people, also through its anti-inflammatory and anti-clotting effects. Fascinating studies imply that supplementing antidepressants with anti-inflammatory drugs can improve their efficacy. Vaccines for infectious diseases such as malaria and HIV are imminent, exploiting the power of the immune system to control and eradicate disease. It couldn’t happen at a better time; as antibiotics become increasingly ineffective due to widespread resistance, a problem classified by the World Health Organization as a "global threat," we will have to develop vaccines to fill that gap too. All of these fields are converging in ways we haven’t seen previously; oncologists, parasitologists, neurobiologists, and infectious disease specialists are all collaborating with immunologists.

It is an exciting time in biology and medicine. The new discoveries of the breadth and potential of our immune response merely hint at the revelations to come. These research findings will always capture the public’s attention, and always be newsworthy, because they represent the tantalizing hope that we can endure beyond disease, and prolong life. However, we must be careful, because the dismaying social success of sham medicine, with its "miracle cures" and "immune boosting diets," highlights how easily this message can be distorted. If we can drive research forward, while communicating it effectively, we may be on the cusp of another revolutionary period in biomedical science.

The big news on April 2, 2013 was the announcement of the BRAIN Initiative from the White House, whose goal is to develop innovative neurotechnology for understanding brain function. Grand challenges like this one happen once every few decades, including the announcement in 1961 of the Apollo Program to land a man on the moon, the War on Cancer in 1971, and the Human Genome Project in 1990. These were ten to fifteen year national efforts that brought together the best and the brightest to attack a problem that could only be solved on a national scale.

Why the brain? Brains are the most complex devices in the known universe and up until now our attempts to understand how brains work have fallen short. It will take a major international effort to crack the neural code. Europe weighed in earlier with the Human Brain Project and Japan later announced a Brain/MINDS project to develop a transgenic nonhuman primate model. China is also planning an ambitious brain project.

Brain disorders are common and devastating. Autism, schizophrenia, and depression destroy personal lives and create an enormous economic burden on society. The annual cost of maintaining patients in the United States with Alzheimer’s disease, a neurodegenerative disorder with no known cure, is $200 billion and climbing as our population ages. Unlike heart diseases and cancers that lead to rapid death, patients can live for decades with brain disorders. The best efforts of drug companies to develop new treatments have failed. If we don’t find a better way to treat broken brains now our children will bear a terrible burden.

A second motivation for reaching a better understanding of brains is to avert a catastrophic collapse of civilization, which is happening in the Middle East. The Internet has made it possible for terrorist groups to proliferate, and modern science has created weapons that pose existential threats ranging from nuclear weapons to genetic recombination. The most versatile weapon delivery system is the human. We need to better understand what happens in the brain of a suicidal terrorist planning to maximize destruction.

These motivations to understand the brain are based on brains behaving badly, but the ultimate scientific goal is to discover the basic principles of normal brain function. Richard Feynman once wrote: "What I cannot create, I do not understand." That is, if you can’t prove something yourself you don’t really understand it. One way to prove something is to build a device based on what you understand and see if it works. Once we have truly uncovered the principles of how the brain works we should be able to build devices with similar capabilities. This will have a profound impact on every aspect of society and the rise of artificial intelligence based on machine learning is a harbinger. Our brain is the paramount learning machine.

These are the goals of the BRAIN Initiative, but its impact may be quite different from our expectations. The goal of the Apollo Project was to send a man to the moon. Mission accomplished, but if the moon was so important why have we not gone back there? In contrast, the impact of building the technologies needed to reach the moon has been far reaching: A thriving satellite industry and advances in digital communications, microelectronics, and materials science, as well as a revamping of the curriculum in science and engineering. The War on Cancer is still being fought, but the invention of recombinant DNA technology allowed us to manipulate the genome and created the biotechnology industry. The goal of the Human Genome Project was to cure human diseases, which we now know are not easily deciphered by reading the base pairs, but the sequencing of the human genome has transformed biology and created a genomic industry that is making possible personalized, precision medicine.

The impact of the BRAIN Initiative will be the creation of neurotechnologies that match the complexity of the brain. Genetic studies have uncovered hundreds of genes that contribute to brain disorders. Drugs have not been as effective in treating brain disorders as they have for heart diseases because of the diversity of cell types in the brain and complexity of the signaling pathways. The development of new neurotechnologies will create tools that are more precisely targeted at the sources of brain disorders. Tools from molecular genetics and optogenetics are already giving us an unprecedented ability to manipulate neurons and more powerful tools are on the way from the BRAIN Initiative.

An important lesson from the history of national grand challenges is that there is no better way to invest in the future than focusing the best and brightest minds on an important problem and building the infrastructure needed to solve the problem.

There has not been a new effective therapy for any neurodegenerative disease in decades. Recent trials of drugs for Alzheimer’s disease have been disappointments. Because of these very expensive failures, many of the big pharmaceutical companies have moved away from targeting brain diseases to more profitable areas like cancer. So is there any good news on the horizon for the millions who are suffering and will suffer from these devastating brain disorders?

This year there was news that a cancer drug showed remarkable benefits for patients with Parkinson’s disease. It was only one, nonrandomized, nonblinded, non–placebo-controlled study that looked at only a few patients. So it’s very early to know whether it really works. But it’s news to follow, and it’s big for three reasons.

First, unlike any other treatment, the drug appears to work close to the root cause of Parkinson’s disease. Parkinson’s is one of the few neurodegenerative disorders for which there is any effective treatment. In Parkinson’s, the neurons that supply the brain with the neurotransmitter, dopamine, degenerate. The mainstay of treatment for the disease has been to replace that missing dopamine with a pill that provides a chemical that is converted in the brain into dopamine. This "dopamine replacement therapy" treats the symptoms of Parkinson’s—the tremors, the stiffness, and the slowness of movements—but not its root cause. So the death of the dopamine-containing neurons continues unabated, and the pills only work well for around seven years.

The new drug, called nilotinib, was developed for leukemia and has the same action as the better known chemotherapeutic agent, Gleevec. But unlike other similar drugs, nilotinib gets across the blood-brain barrier, which prevents most drugs from working well in the brain. Although the cause of the neuronal degeneration of Parkinson’s is still not known, it is thought to involve the accumulation and misfolding of proteins inside the dying neurons, a process like the curdling of the proteins in milk. Nilotinib was predicted to suppress the accumulation of misfolded proteins inside neurons. After taking nilotinib, the patients not only did better clinically, but the amount of the misfolding proteins released into the patients’ cerebral spinal fluid went down—a sign that it was working on the degenerative process, itself.

Second, the target that nilotinib inhibits is a new one for a brain disease. Like Gleevec, nilotinib inhibits an enzyme inside the cell called a protein kinase. There are around 500 different kinds of protein kinases in cells, and nilotinib targets one of them. But whereas there are many kinases in a cell, there are far more biochemical functions that a cell has to do. So most kinases have many functions, some seemingly unrelated. Scientists focused on the kinase that nilotinib inhibits, because if it becomes overactive it can drive unchecked growth of white cells in the blood, causing leukemia. But they also found that it is involved in the accumulation of neuronal proteins that can get misfolded. Nilotinib is big news because drugs that target kinases are relatively easy to develop, and nilotinib provides the first example showing that if they work for one disease, they might be used for a second seemingly unrelated disease. At the bedside, leukemia and Parkinson’s seem as far apart as you can get.

Third, the timing with which the drug may work tells us something new and exciting about Parkinson’s itself, which might be relevant to other neurodegenerative diseases such as Alzheimer’s. Protein misfolding in neurons seems a very general process in many neurodegenerative disorders. But no one knows whether suppressing protein misfolding will result in the slowing or stopping of a disease, or even in recovering function. The effect of nilotinib seems relatively fast—the trial was only for a few months. If nilotinib’s beneficial action to patients is really on inhibiting the accumulation and misfolding of neuronal proteins (and not secondarily on increasing the release of dopamine), and if the patients really improved, this could mean that the misfolding is one side of an active and dynamic battle in neurons between "good" folding and "bad" folding. In that case, there would be processes in neurons that are actively trying to repair the cell. This gives us hope for a cure and restoration of lost function in many neurological diseases.

It was just one among dozens and dozens of revelations about the National Security Agency, barely enough to cause a stir in the papers. Yet it is a herald of a new era.

In May, 2014, journalists revealed that the NSA was recording and archiving every single cell phone conversation that took place in the Bahamas. Now, the Bahamas isn't a very big place, with only a few hundred thousand people on the islands at any given time. Nor is it very often the source of international headlines. So, at first glance, the NSA's achievemment might not look like much. But in capturing—and storing—all of the Bahamas' cell phone conversations in real time, the NSA has managed to transform a significant proportion of the day-to-day interactions of a society into data: into information that can be analyzed and transformed and correlated and used to understand the people who produced that information. This was something unthinkable even a decade or two ago, yet, almost unnoticed, the processing power of computers, the scale of their memory banks, and the cheapness and ubiquity of their sensors, are making civilization-scale data gathering almost routine.

NSA's collection program in the Bahamas—codenamed SOMALGET—was a small part of a larger operation, which, itself, is just a tiny fraction of the NSA's global surveillance system. Whistleblowers and leaked documents have revealed that NSA has been gathering and storing e-mails, phone calls, and other records on a global scale, and, apparently to capture entire nations' communications outputs and store it for later study. And the NSA isn't the only entity with such ambition. Other agencies and companies around the world have been collecting and creating datasets that capture one entire facet of the behavior of millions or even billions of people. The city of New York can now analyze each taxi ride taken in the five boroughs over the past several years. Google has stored every single character that anyone has entered into its search engine for more than a decade. It's all in there, taking up much less room in memory than you might think.

A medical researcher can now download and analyze all the drug prescriptions filled in the United Kingdom, an epidemiologist can view all the deaths recorded in the United States, a civil engineer can view all airline flights taken anywhere in the world at any time in recent history. Personal genomics companies are now performing cut-rate genomic analysis of more than one million customers; at this point, it's just cost and desire that prevents us from capturing the entire genome of every individual on Earth. And as digital cameras, microphones, and other sensors are woven into every aspect of the fabric of our society, we are not far from the point from when we're able to capture the movements and utterances of every single macroscopic creature in the places we inhabit.

Pretty much anything that can be digitized or digitally collected, and numbers in the billions or trillions or quadrillions, can now be archived and analyzed. All our communications, all our purchases, our travels and our daily routines are all now, to at least some degree, sitting on banks of computer memory. We no longer have to guess, to sample, to model; it's all there for the taking. It's only as this data begins to shine light into every corner of our society that we will recognize how much of our existence has been in darkness—or how different life will be in a world without shade.

Could the color of a cheap dress create a meaningful scientific controversy? In 2015 a striped, body-hugging, £50 dress did just that. In February, Scottish mother Cecilia Bleasdale sent her family a poor quality photo of a dress she bought for her daughter’s wedding. Looking at the same image, some people saw the stripes as blue and black; others as white and gold. Quickly posted online, “that dress” was soon mentioned nearly half a million times. This simple photo had everything a meme needs to thrive: it was easy to pass on, accessible to all, and sharply divided opinions. #thedress was indeed called the meme of the year and even a “viral singularity.” Yet it did not die out as fast as it had risen. Unlike most viral memes, this one prompted deeper and more interesting questions.

Scientists quickly picked up on the dispute and garnered some facts. Seen in daylight, the actual dress is indisputably blue and black. It is only in the slightly bleached out photograph that white and gold is seen. In a study of 1,400 respondents who’d never seen the photo before, 57 percent saw blue and black, 30 percent saw white and gold, and about 10 percent saw blue and brown. Women and older people more often saw white and gold.

This difference is not like disputes over whether the wallpaper is green or blue. Nor is it like ambiguous figures such as the famous Necker cube, which can be seen tilted towards or away from the viewer, or the duck/rabbit or wife/mother-in-law drawings. People can typically see these bistable images either way, and flip their perception between views, getting quicker with practice. Not so with “that dress.” Only about 10 percent of people could switch colors. Most saw the colors resolutely one way and remained convinced that they were “right.” What was going on became a genuinely interesting question for the science of color vision.

Vision science has long shown that color is not the property of an object, even though we go on speaking as though it is. In fact, color emerges from a combination of the wavelengths of light emitted or reflected from an object and the kind of visual system looking at it. A normal human visual system, with three cone types in the retina, concludes “yellow” when any one of an indefinite number of different wavelength combinations affects its color-opponent system in a certain way. This means that a species with more cone types, such as the mantis shrimp which has about sixteen types, would see many different colors when humans would see only the same shade of yellow.

When people are red-green “color blind,” with only two cone types instead of three, we may be tempted to think they fail to see its real color. Yet there is no such thing. There are even rare people (mostly women) who have four cone types. Presumably they can see colors that the rest of us cannot even imagine. This may help us accept that the dress is not intrinsically one color or the other, but still provides no clue as to why people see it so differently.

Could the background in the photo be relevant? In the 1970s, Edwin Land (inventor of the Polaroid camera) showed that the same colored square appears to be a different color depending on the other squares surrounding it. This relates to an important problem that evolution has had to solve. If color information is to be useful, an object must look the same color on a bright sunny day as on an overcast one, yet the incident light is yellower at midday and bluer from a gloomy or evening sky. So our visual systems use a broad view of the scene to assess the incident light and then discount that when making color decisions, just like the automatic white balance (AWB) in modern cameras.

This, it turns out, may solve the great dress puzzle. It seems that some people take the incident light as yellowish, discounting the yellow to see blue and black, while others assume a bluer incident light and see the dress as white and gold. Do the age and sex differences provide any clues as to why? Are genes or people’s lifetime experiences relevant? This controversy is still stimulating more questions.

Was it a step too far when some articles suggested that #thedress could prompt a "world-wide existential crisis" over the nature of reality? Not at all, for color perception really is strange. When philosophers ponder the mysteries of consciousness they may refer to qualia—private, subjective qualities of experience. An enduring example is “the redness of red” because the experience of seeing color provokes all those questions that make the study of consciousness so difficult. Is someone else’s red like mine? How could I find out? And why, when all this extraordinary neural machinery is doing its job, is there subjective experience at all? I would guess that “that dress” has yet more fun to provide.

The news cycle regularly features stories about reinventing 21st century cities. Amongst societal issues, perhaps only education is targeted more frequently for reform. And for good reason.

Since 2008, more people have lived in cities than not. By the end of this century, cities will generate nearly 90 percent of population growth and 60 percent of energy consumption. While these bustling hubs of humanity function as the planet’s innovation centers, they’re also responsible for the lion’s share of environmental damage. By some estimates, today’s cities generate around 75 percent of global carbon dioxide emissions, alongside countless other pollutants. They consume vast expanses of forests, farmland, and other landscapes while fouling rivers, oceans, and soils. In short, if we don’t get cities right, it’s hard to imagine a healthy future for humanity, let alone the biosphere.

A Tale of Two Cities

By my reading, most of the press surrounding the reinvention of cities can be grouped into two seemingly disparate camps. One camp calls for cities that are “smart,” “digital,” and “high-tech.” Here the emphasis is on information and communication technologies with the potential to boost urban functioning. Fueled by the recent tsunami of civic data—climate information, traffic patterns, pollution levels, power consumption, etc.—key arenas cited for high-tech interventions include flows of people, energy, food, water, and waste. Advocates imagine cities that can talk, providing live status updates for pollution, parking, traffic, water, power, and light. Thanks to such innovations as ultra-low power sensors and web-based wireless networks, smart cities are rapidly becoming reality.

From the other camp we hear about the need for “green,” “biophilic,” even “wild” cities where nature is conserved, restored, and celebrated. Of course, cities have traditionally been places where the wild things aren’t, engineered to wall humans off from the natural world. Yet recent and rapidly accumulating research documents the positive health impacts of regular contact with urban nature. Benefits include reduced stress levels, stronger immune systems, and enhanced learning. Perhaps most important are the myriad physical, mental, and emotional benefits that appear to be essential to a healthy childhood. Proponents of the green city camp also point out that many of the pressing issues of our time—among them climate change, species extinctions, and habitat loss—will not, indeed cannot, be addressed unless people understand and care about nearby nature.

So there you have it. Big Data versus Mother Nature. Two views on the future of cities, apparently residing at opposite ends of the spectrum. One values technological innovation, the other biological wisdom and nature connection. Yet, upon closer inspection, these perspectives are far from mutually exclusive. Indeed they’re complementary.

High-Tech Meets Nature-Rich

It’s entirely possible for cities to be both high-tech and nature-rich. Today, few proponents of green cities claim that we need to go “back to nature.” Rather they argue for going forward into a future rich in both technology and nature. New terms like “technobiophilic cities” and “nature-smart cities” are emerging to describe this blended concept, urban settings where the natural and digital are embraced simultaneously.

Yes, nature-smart cities will have plenty of green roofs, green walls, and interconnected green spaces. Seeding native plants attracts native insects, which in turn entices native birds and other animals, transforming backyards, schoolyards, and courtyards into miniature ecosystems. These nuggets of urban nature, in addition to improving the health of humans, are the last good hope for scores of threatened species.

In addition, cities rich in nature can leverage smart technologies to help urbanites switch to renewable energy sources—wind, sun, water, and geothermal. Green transportation reduces carbon emissions and improves the environment. Green buildings can act like trees, running on sunlight and recycling wastes, so that cities function like forests.

Interestingly, both views of our collective urban future highlight the importance of an informed and engaged citizenry. Digital technologies and big data have potential to put control back in the hands of individuals—for example, through greater participation in local governance (“E-Governance”). Similarly, citizen scientists and citizen naturalists can play important roles restoring plants and animals, as well as monitoring these species and making adjustments to improve the quality and quantity of nearby nature. Here, then, is a potent pathway to help people act on the basis of robust scientific data (and boost science literacy along the way).

In short, there’s much more than hot air in all the news about reimagining the future of cities. At least within urban settings, Mother Nature and Big Data have the potential to make excellent bedfellows. Indeed our survival, and that of much of Earth’s biodiversity, may just depend on consummating this union. If successful, we’ll witness the birth of a new kind of city, one in which both people and nature thrive.

Behind every great scientific discovery is an instrument. From Galileo and his telescope to Arthur Compton and the cloud chamber, our most important discoveries are underpinned by device innovations that extend human senses and augment human cognition. This is a crucially important science news constant, because without new tools discovery would slow to a crawl. Want to predict the next big science surprise a decade from now? Look for the fastest-moving technologies in the present and ask what new tools they enable.

For the last half-century, digital technology has delivered the most powerful tools in the form of processors, networking, and sensors. Processing came first, providing the brains for space probes and the computational bulldozers needed for tackling computation-intensive research. Then with the advent of the Arpanet, the Internet, and the World Wide Web, networking became a powerful medium for accessing and sharing scientific knowledge—and connecting remotely to everything from supercomputers to telescopes.

But it is the third category—sensors, and an even-newer category of robust effectors that are poised to accelerate and utterly change research and discovery in the decades ahead.

First, we created our computers, then we networked them together, and now we are giving them sensory organs to observe—and manipulate—the physical world in the service of science. And thanks to the phenomenon described by Moore’s law, sensor cost/performance is racing ahead as rapidly as chip performance. Ask any amateur astronomer: for a few thousand dollars, they can purchase digital cameras that were beyond the reach of observatories a decade ago.

The entire genomics field owes its very existence and future to sensors. Craig Venter’s team became the first to decode the human genome in 2001 by leveraging computational power and sensor advances to create a radically new—and radically less expensive—sequencing process. Moreover, the cost of sequencing is already dropping more rapidly than the curve of Moore’s Law. Follow out the Carlson Curve (as the sequencing price/performance curve was dubbed by The Economist), and cost of sequencing a genome is likely to plummet below one dollar well before 2030. Meanwhile, the gene editing made possible by the CRISPR/Cas system is possible only because of ever more powerful and affordable sensors and effectors. Just imagine the science that is possible when sequencing a genome costs a dime and networked sequencing labs-on-a-chip are cheap enough to be tossed out and discarded like RFID tags.

Sensors and digital technology are also driving physics discovery. The heart of CERN’s Large Hadron Collider is the CMS detector, a 14,000-tonne assemblage of sensors and effectors that has been dubbed "science’s cathedral." Like a cathedral of old, it is served by nearly 4,000 people drawn from over forty countries, and it is so popular that a scientific journal just featured a color-in centerfold of the device in its year-end issue.

Sensors are also opening vast new windows on the cosmos. Thanks to the relentless advance of sensors and effectors in the form of adaptive optics, discovery of extrasolar planets moved from science fiction to commonplace with breathtaking speed. In the very near future, sensor advances will allow us to analyze exoplanetary atmospheres and look for civilizational signatures. The same trends will open new horizons for amateur astronomers, who will soon enjoy affordable technical means to match the Kepler spacecraft in planet-finding prowess. Sensors are thus as much about democratizing amateur science as the creation of ever more powerful instruments. The Kepler satellite imaged a field of 115 degrees, or a mere 0.25 percent of the sky. Planet-finding amateurs wielding digitally empowered backyard scopes could put a serious dent in the other 99.75 percent of the sky yet to be examined.

Another recent encounter between amateurs and sensors offers a powerful hint of what is to come. Once upon a time, comets were named after human discoverers, and amateurs hunted comets with such passion that more than one would-be comet hunter relocated their residence eastwards in order to get an observing jump on the competition. Now, comets have names like 285P/Linear because robotic systems are doing the discovering, and amateur comet hunting is in steep decline. Amateurs will find other things to do, (like search for planets) but it is hard not to feel a twinge of nostalgia for a lost time when that wispy apparition across the sky carried a romantic name like Hale-Bopp or Ikeya-Seki rather than C/2011-L4 PanStarrs.

This shift in cometary nomenclature hints at an even more dramatic sea change to come in the relationship between instrument and discoverer. Until now, the news has been of ever more powerful instruments created in the service of amplifying human-driven discovery. But just as machines today are better comet finders than humans, we are poised on the threshold of a time when machines do not merely amplify but displace the human researcher. When that happens, the biggest news of all will be when a machine wins a Nobel Prize alongside its human collaborators.

The most thought-provoking scientific meeting I went to in 2015 was Emergent Quantum Mechanics, organised in Vienna by Gerhard Groessing. This is the go-to place if you’re interested in whether quantum mechanics dooms us to a universe (or multiverse) that can be causal or local but not both, or whether we might just make sense of it after all. The big new theme was emergent global correlation. What is this, and why does it matter?

The core problem of quantum foundations is the Bell tests. In 1935, Einstein, Podolsky and Rosen noted that if you measured one of a pair of particles that shared the same quantum mechanical wave function then this would immediately affect what could be measured about the other, even if it were some distance away. Einstein held that this “spooky action at a distance” was ridiculous, so quantum mechanics must be incomplete. This was the most cited paper in physics for decades. In 1964 the Irish physicist John Bell proved that if particle behavior were explained by hidden local variables, their effects would have to satisfy an inequality that would be broken in some circumstances by quantum mechanical behavior. In 1974, Clauser, Horne, Shimony, and Holt proved a related theorem that limits the correlation between the polarization of two photons, assuming that this polarization is carried entirely by and within them. Freedman and Clauser showed this was violated experimentally, followed by Aspect, Zeilinger and many others. These “Bell tests” convince many physicists that reality must be weird; maybe non-local, non-causal, or even involving multiple universes.

For example, it’s possible to entangle photon A with photon B, then B with C, then C with D, and measure that A and D are correlated, despite the fact that they didn’t exist at the same time. Does this mean that when I measure D, some mysterious influence reaches backward in time to A? The math doesn’t let me use this to send a message backwards in time to order the murder of my great-grandfather (the no-signalling theorem becomes a kind of “no-tardis theorem”) but such experiments are still startlingly counterintuitive.

At EMQM15, a number of people advanced models according to which quantum phenomena emerge from a combination of local action and global correlation. As the Nobel prizewinner Gerard ‘t Hooft put it in his keynote talk, John Bell assumed that spacelike correlations are insignificant, and this isn’t necessarily so. In Gerard’s model, reality is information, processed by a cellular automaton fabric operating at the Planck scale, and fundamental particles are virtual particles—like Conway’s gliders but in three dimensions. In a version he presented at the previous EMQM event in 2013, the fabric is regular and its existence many break gauge invariance just enough to provide the needed long-range correlation. The problem was that the Lorentz group is open, which seemed to prevent the variables in the automata being bitstrings of finite length. In his new version, the automata are randomly distributed. This was inspired by an idea of Steven Hawking’s on balancing the information flows into and out of black holes.

In a second class of emergence models, the long-range order comes from an underlying thermodynamics. Gerhard Groessing has a model in which long-range order emerges from subquantum statistical physics; Ariel Caticha has a model with a similar flavor, which derives quantum mechanics as entropic dynamics. Ana Maria Cetto looks to the zero-point field and sets out to characterise active zero-point field modes that sustain entangled states. Bei-Lok Hu adds a stochastic term to semiclassical gravity, whose effect after renormalisation is nonlocal dissipation with colored noise.

There are others. The quantum crypto pioneer Nicolas Gisin has a new book on quantum chance in which he suggests that the solution might be nonlocal randomness: a random event that can manifest itself at several locations. My own suspicion is that it might be something less colorful; perhaps the quantum vacuum just has an order parameter, like a normal superfluid or superconductor. If you want long-range order that interacts with quantum systems, we have quite a few examples and analogues to play with.

But whether you think the quantum vacuum is God’s computer, God’s bubble bath, or even God’s cryptographic keystream generator, there’s suddenly a sense this year of excitement and progress, of ideas coming together, of the prospect that we might just possibly be able to replace magic with mechanism.

There may be a precedent. For about forty years after Galileo, physics was a free-for-all. The old Ptolemaic certainties had been shot away and philosophers’ imaginations ran wild. Perhaps it would be possible, some said, to fly to America in eight hours in a basket carried by swans? Eventually, Newton wrote the Principia and spoiled all the fun. Theoretical physics has been stuck for the past forty years, and imaginations have been running wild once more. Multiple universes that let stuff travel backwards in time without causing a paradox? Or perhaps it’s time for something new to come along and spoil the fun.

We used to think of memory as a veridical record of events past, like a videotape in our heads that is always on hand to be re-played. Of course, we knew this memory to be far more fragile and incomplete than a real videotape: we forget things, and many events don’t even get stored in the first place. But when we replay our memories, we feel sure that what we do recall really happened. Indeed, our entire legal system is built on this belief.

Three scientific discoveries in the past century have changed that picture: two some time ago, and one—the “news”—that is very recent. Some time ago, we learned that memory is not a record so much as a re-construction. We don’t recall events so much as reassemble them, and in doing so, crucial aspects of the original event may get substituted—it wasn’t Georgina you ran into that day, it was Julia; it wasn’t Monte Carlo, it was Cannes; it wasn’t sunny, it was actually overcast—it rained later, remember? Videotapes never do that—they get ragged and perhaps skip sections or lose information but they don’t make things up.

It has also been known for some time now—since the 1960s in fact—that the act of re-activating a memory renders it temporarily fragile, or “labile.” In its labile state, a memory is vulnerable to disruption, and might be stored again in an altered form. In the laboratory, this alteration is usually a degradation, induced by some memory-unfriendly agent like a protein-synthesis inhibitor. We knew such drugs could affect the formation of memories, but it is more surprising that they can also disrupt a memory after it has been formed, albeit only when it has been re-activated.

The story doesn’t end there. Very recently, it has been shown that memories aren’t just fragile when they have been re-activated, they can actually be altered. Using some of the amazing new molecular genetic techniques that have been developed in the past three decades, it has become possible to identify which subset of neurons participated in the encoding of an event, and later experimentally re-activate only these specific neurons, so that the animal is forced (we believe) to recall the event. During this re-activation, scientists have been able to tinker with these memories so that they are different from the original ones. So far, these tinkerings have just involved changing emotional content, such that, for example, a memory for a place that was neutral becomes positive, or for one that was positive becomes negative, so that the animal subsequently seeks out or avoids those places. However, we are not far from trying to actually write new events into these memories, and it seems likely that this will be achievable.

It may seem odd that memories are so plastic and vulnerable to change. Why would we evolve a strange, disconcerting, system like this? Why can’t memory be more like a videotape, so that we can trust it more? We don’t know the answer for sure yet, but evolution doesn’t care about veracity, it only cares about survival, and it usually has a good reason for what may seem like odd design features.

The advantages of the constructive nature of memory seem obvious: rather than the enormous storage capacity required to remember every “pixel” of a life experience, it is a far more economical use of our synapses to stockpile a collection of potential memory ingredients, and then to simply record each event in the form of a recipe: take a pinch of a Southern French beach, add a dash of an old school friend, mix in some summer weather… etc. The labile nature of memory is more curious, but many theoretical neuroscientists think it may allow construction of super-memories, called semantic memories, which are agglomerations of individual event-memories that combine to form a more general, overarching piece of knowledge about the world. After a few visits to the Mediterranean you learn that it is usually sunny, and so the odd incidence of overcast gloom gets washed out, and fades from recollection. Your behavior thus becomes adapted not to a specific past event but to the general situation, and you know on holiday to pack sunscreen and not umbrellas.

The fabricated, labile nature of memory is at once a reason for amazement and concern. It is amazing to think that the brain in constantly and busily re-assembling our past, such that that past is not really the one we think it is. It is also concerning, because this constructed past seems extraordinarily real—almost as real as our present—and we base our behavior on it trustingly.

Thus, an eye-witness will make confident assertions based solely on recollection that lead to the lifelong incarceration of another person, and nobody worries about this except neuroscientists. It is also amazing/concerning that as scientists and doctors, we are now on the threshold of memory editing: being able to alter a person’s life memories in a selective manner.

The therapeutic potential of this is exciting—imagine being able to surgically reduce the pain of a traumatic memory? But we could also reach into the brain and change a person’s past, and in doing so change who they are. These are technologies to use with care. However, one could argue that the fabricated and labile nature of our memories means that perhaps we aren’t really who we think we are, anyway.

New tools and techniques in science don’t usually garner as much publicity as big discoveries, but there is a sense in which they are much more important. Think of telescopes and microscopes: both opened vast fields of endeavour that are still spawning thousands of major advances. And although they may not make newspaper front pages, new tools are often the biggest news in scientists' own ratings—published in prestigious journals and staying at the top of citation indices for years on end. Clever tools are the really long-lasting news behind the news, driving science forward for decades.

One neat example has just come along, which I really like. A new technique makes it possible to see directly the very fast electrical activity occurring within the nerve cells of the brain of a living, behaving animal. Neuroscientists have had something like this on their wish list for years and it’s worth celebrating. The technique puts into nerve cells a special protein that can turn the tiny voltage changes of nerve activity into flashes of light. These can be seen with a microscope and recorded in exquisite detail, providing a direct window into the activity of a brain and the fine dynamics of signals travelling through nerves. That is especially important because the hot news is that information contained in the nerve pulses speeding around the brain is likely coded, not just in the rate at which those pulses arrive but also their timing, with the two working at different resolutions. To start to speak neuron and thus understand our brains, we are going to have to get to grips with the dynamics of signalling and relate it to what an animal is actually doing.

The new technique, developed by Yiyang Gong and colleagues in Mark Schintzer’s lab at Stanford University, and published in the journal Science, builds on past tools for imaging nerve impulses. One well-established method takes advantage of the calcium ions which rush into a nerve cell as a signal speeds by. Special chemicals that give off light when they interact with calcium make that electrical activity visible, but they're not fast or sensitive enough to capture the speed with which the brain works. The new technique goes further by using a rhodopsin protein (called Ace), which is very sensitive to voltage changes in the nerve cell membrane, fused to another protein (mNeon), which can fluoresce very brightly. The combination is both fast and bright.

This imaging technique will take its place alongside other recent developments that extend the neuroscientist’s reach. New optogenetic tools are truly stunning; they enable researchers to use light signals to switch particular nerve cells off and on to help figure out what part they play in a larger circuit.

Without constantly inventing new ways to probe the brain, the eventual goal of understanding how our 90 billion nerve cells provide us with thought and feeling will be utterly intractable. Although we have some good insights into our cognitive strategies from psychology, deep understanding of how individual neurons work and rapidly growing maps of brain circuitry, the vital territory in the middle—how circuits of particular linked neurons work—is very tough to explore. To make progress, neuroscientists' dream of experiments where they can record what is happening in many nerves in a circuit, while also switching parts of the circuit off and on, and seeing the impact on a living animal’s behavior. Thanks to new tools, this remarkable dream is coming close, and when the breakthroughs arrive, the toolmakers will once again have proved that in science, it is new tools that create new ideas.

In terms of sheer unfulfilled promise, interdisciplinary research has to stand as one of the most frustrating examples in the world of social research. The promise can be put simply. The challenges we face in modern society, from responding to climate change through to anti-microbial resistance via so many issues to do with economic, social, political, and cultural well being do not come in disciplinary packages. They are complex and require an integrated response drawing on different levels of enquiry. And yet we persist in organizing ourselves in academic siloes and risk looking like those blind men groping an elephant. As Garry Brewer pithily observed back in 1999, “The world has problems, universities have departments.”

The reasons this promise lies unfulfilled are equally clear. Building an academic career requires immersion in a speciality with outputs (articles, books, talks) that win the approval of peers. Universities are structured in terms of departments, learned societies champion a single discipline, and funding agencies prioritize specific work from those who have built the right kind of credibility in this context. There is, quite literally, a coordination problem here. And this means interdisciplinary work is hard to do well, often falling between stools and sometimes lost in arcane debate about its very nature swapping “inter” for “multi,” “cross,” “trans,” “post,” and other candidate angels to place on the head of this pin.

This isn’t equally true for all disciplines. Some have overcome these hurdles for years—neuroscience, bioinformatics, cybernetics, and biomedical engineering, and more recently we have seen economics taking a behavioral turn while moral philosophy has drawn increasingly on experimental psychology. However, the bulk of the social sciences have proved peculiarly resistant despite how suitable their problem domains are to multi-level inquiry.

The good news is we are seeing substantial shifts in this terrain that could last, triggered in part by the rise of big data and new technology. Social researchers are agog at the chance to listen to millions of voices, observe billions of interactions, and to analyze patterns at a scale never seen before. But to engage seriously requires new methods and forms of collaboration, with a consequent erosion of the once insurmountable barrier between quantitative and qualitative research. An example comes from Berkeley, where Nick Adams and his team are analyzing how violence breaks out in protest movements—an old sociological question, but now with a database (thanks to the number of Occupy movements in the US) that is so large the only way to analyze the material feasibly requires a Crowd Content Analysis Assembly Line (combining crowd sourcing and active machine learning) to code vast corpora of text. This new form of social research, drawing on computational linguistics and computer science to convert large amounts of text into rich data, could lead to insights in a vast array of social and cultural themes of our time.

Moreover these shifts might stick if we continue to see centers of excellence focusing on data intensive social research, like D-Lab at Berkeley or the Institute for Quantitative Social Science at Harvard show how institutions can reconfigure themselves to respond to this opportunity. As Gary King (Director of the latter) has put it:

The social sciences are undergoing a dramatic transformation from studying problems to solving them; from making do with a small number of sparse data sets to analyzing increasing quantities of diverse, highly informative data; from isolated scholars toiling away on their own to larger scale, collaborative, interdisciplinary, lab-style research teams; and from a purely academic pursuit focused inward to having a major impact on public policy, commerce and industry, other academic fields, and some of the major problems that affect individuals and societies.

More structural change will follow these innovations. Universities around the world having long invested in social science infrastructure are looking to these models so as to combine and merge efforts more effectively via multi-disciplinary research centers and collaborative teams. And we are seeing changes in funders’ priorities too. The Wellcome Trust, for instance, now offers the Hub Award to support work that “explores what happens when medicine and health intersect with the arts, humanities and social sciences.”

Of course the biggest shaper of future research comes at a national level. In the UK the proposed implementation of a “cross-disciplinary fund” alongside a new budget to tackle “global challenges” may indicate the Government’s seriousness of interdisciplinary intent. Details will follow, and they may prove devilish. But the groundswell of interest, sustained by opportunities in data intensive research, is undeniable.

So interdisciplinary social research should increasingly become the norm, notwithstanding the fact that specialism will still be important. After all we need good disciplines to do good synthetic work. But if this hope is fulfilled we might see how social sciences could coalesce into a more singular social science and be more fully engaged with problem domains first, and departmental siloes second.

Consider a thought experiment: If market capitalism was the brief product of happy coincidences confined in space and time to the developed world of the 19th-20th centuries, (but that were no longer true under 21st century technology) what would our world look like if there were no system to take its place? I have been reluctantly forced to the conclusion that if technology had killed capitalism, economic news would be indistinguishable from today’s feed.

Economic theory, like the physics on which it is based, is in essence an extended exercise in perturbation theory. Solvable and simplified frictionless markets are populated by rational agents which are then all subjected to perturbations in an effort to recover economic realism. Thus while it is false that economists believe idealized models to be exactly accurate as outsiders contend, it is fair to say that they implicitly assume deviations from the ideal are manageably small. Let us list a few such heuristics that may have recently been approximately accurate, but which are not enforced by any known law:

Wages set to the marginal product of labor are roughly equal to the need to consume at a societally acceptable level.

Prices and outputs fluctuate coherently so that it is meaningful to talk of scalar rates of inflation and growth (rather than varying field concepts like temperature or humidity).

Growth can be both high and stable with minimal interference by central banks.

The anthropic viewpoint on such heuristics, more common in physics than economics, would lead us to ask “Is society now focused on market capitalism because it is a fundamental theory, or because we have just lived through the era in which it was possible due to remarkable coincidences?”

To begin to see the problem, recall that in previous eras innovations created high value occupations by automating or obviating those of lower value. This led to a heuristic that those who fear innovation do so because of a failure to appreciate newer opportunities. Software, however is different in this regard and the basic issue is familiar to any programmer who has used a debugger. Computer programs, like life itself, can be decomposed into two types of components:

Loops which repeat with small variations.

Rube Goldberg like processes which happen once.

If you randomly pause a computer program, you will almost certainly land in the former because the repetitive elements are what gives software its power, by dominating the running time of most all programs. Unfortunately, our skilled labor and professions currently look more like the former than the latter, which puts our educational system in the crosshairs of what software does brilliantly.

In short, what today’s flexible software is threatening is to “free” us from the drudgery of all repetitive tasks rather than those of lowest value, pushing us away from expertise (A) which we know how to impart, toward ingenious Rube Goldberg like opportunities (B) unsupported by any proven educational model. This shift in emphasis from jobs to opportunities is great news for a tiny number of creatives of today, but deeply troubling for a majority who depend on stable and cyclical work to feed families. The opportunities of the future should be many and lavishly rewarded, but it is unlikely that they will ever return in the form of stable jobs.

A next problem is that software replaces physical objects by small computer files. Such files have the twin attributes of what economists call public goods:

The good must be inexhaustible (my use doesn’t preclude your use or reuse).

The good must be non-excludable (the existence of the good means that everyone can benefit from it even if they do not pay for it).

Even die-hard proponents of market capitalism will cede that this public sector represents “market failure” where price and value become disconnected. Why should one elect to pay for an army when he will equally benefit from free riding on the payments of others? Thus in a traditional market economy, payment must be secured by threat of force in the form of compulsory taxes.

So long as public goods make up a minority of a market economy, taxes on non-public goods can be used to pay for the exception where price and value gap. But in the modern era, things made of atoms (e.g. vinyl albums) are being replaced by things made of bits (e.g. MP3 files). While 3D printing is still immature, it vividly showcases how the plans for an object will allow us to disintermediate its manufacturer. Hence, the previous edge case of market failure should be expected to claim an increasingly dominant share of the pie.

Assuming that a suite of such anthropic arguments can be made rigorous, what will this mean? In the first place, we should expect that because there is as yet no known alternative to market capitalism, central banks and government agencies publishing official statistics will be under increased pressure to keep up the illusion that market capitalism is recovering by manipulating whatever dials can be turned by law or fiat, giving birth to an interim “gimmick economy”.

If you look at your news feed, you will notice that the economic news already no longer makes much sense in traditional terms. We have strong growth without wage increases. Using Orwellian terms like “Quantitative Easing” or “Troubled Asset Relief”, central banks print money and transfer wealth to avoid the market’s verdict. Advertising and privacy transfer (rather than user fees) have become the business model of last resort for the Internet corporate giants. Highly trained doctors squeezed between expert systems and no-frills providers are moving from secure professionals towards service sector-workers.

Capitalism and Communism which briefly resembled victor and vanquished, increasingly look more like Thelma and Louise; a tragic couple sent over the edge by forces beyond their control. What comes next is anyone’s guess and the world hangs in the balance.

The decade of the brain is maturing into the century of the mind. New bioengineering techniques can resolve and perturb brain activity with unprecedented specificity and scope (including neural control with optogenetics, circuit visualization with fiber photometry, receptor manipulation with DREADDs, gene sculpting with CRISPR/Cas9, and whole brain mapping with CLARITY). These technical advances have captured well-deserved media coverage and inspired support for brain mapping initiatives. But conceptual advances are also needed. More rapid progress might occur by complementing existing “broad science” initiatives with “deep science” approaches capable of bridging the chasms that separate different levels of analysis. Thus, some of the most interesting neuroscientific news on the horizon might highlight not only new scientific content (for example, tools and findings) but also new scientific approaches (for example, deep versus broad science approaches).

What is “deep science”? Deep science approaches seek first to identify critical nodes (or units) within different levels of analysis, and then to determine whether they share a link (or connection) across those levels of analysis. If such a connection exists, that might imply that perturbing the lower-level node could causally influence the higher-level node. Some examples of deep science approaches might include using optogenetic stimulation to alter behavior, or using FMRI activity to predict psychiatric symptoms. Because deep science first seeks to bridge different levels of analysis, it often requires collaboration of at least two experts at different levels of analysis.

The goals of deep science stand in contrast to those of broad science. Broad science approaches first seek to map all nodes within a level of analysis as well as links between them (for example, all neurons and their connections in a model organism such as a worm). Comprehensive characterization represents a necessary step towards mapping the landscapes of new data produced by novel techniques. Examples of broad science approaches include connectomic attempts to characterize all brain cells in a circuit, or computational efforts to digitally model all circuit components. Broad science initiatives implicitly assume that by fully characterizing a single level of analysis, a better understanding of higher-order functions will emerge. Thus, a single expert at one level of analysis can advance through persistent application of relevant methods.

Due to more variables, methods, and collaborators, deep science approaches pose greater coordination challenges than broad science approaches. Which nodes to target or levels to link might not be obvious at the outset, and might require many rounds of research. Although neuroscientists have long distinguished different levels of analysis (for instance, Marr’s descending goal, process, and hardware levels of analysis), they have often emphasized one level of analysis to the exclusion of others, or assumed that links across levels were arbitrary and thus not worthy of study. New techniques, however, have raised possibilities for testing links across levels. Thus, one deep science strategy might involve targeting links that causally connect ascending levels of analysis. For instance, recent evidence indicates that optogenetic stimulation of midbrain dopamine neurons (the hardware level) increases FMRI activity in the striatum (the process level), which predicts approach behavior (the goal level) in rats and humans.

While deep science findings are not yet news, I predict they soon will be. Deep science and broad science are necessary complements, but broad science approaches currently dominate. By linking levels of analysis, however, deep science approaches may more rapidly translate basic neuroscience knowledge into behavioral applications and healing interventions—which should be good news for all.

A policeman saw a drunk man looking for something under a streetlight and asked what the drunk lost. The drunk says “I lost my keys” and they both look under the streetlight together. After a while the policeman asks “Are you sure you lost them here?” The drunk replies “No, I lost them in the park.” The policeman says “Why are you searching here?” The drunk replies, "this is where the light is!”

For decades this search strategy has been employed both by drunks and by neutrino hunters, with no keys in sight and few key insights. In 1916 Einstein published the second of his General Relativity papers. One hundred years later, using Einstein’s predictions, we are at the precipice of “weighing” the last elementary particle whose mass is unknown. Isn't this old news? Don’t we know all the fundamental particle masses already after measuring the Higgs boson’s mass? Well, yes and no.

Looking at the Standard Model, we see 16 massive particles (quarks), leptons (like electrons), and bosons (such as the photon), plus the Higgs boson charted together in a table reminiscent of Mendeleev’s Periodic Table of the Elements, except in the case of the quarks, bosons, and leptons on the table, there is no periodicity, no apparent ordering at work here.

Three of the six leptons (“small” in Greek; particles that don’t participate in the Strong nuclear force) are the three “generations” of neutrinos: electron, muon, and tau neutrinos. As integral as they are to the foundations of matter, we are in the dark about their masses. A particle’s mass is arguably its most distinctive property, so this lacuna is rightly seen as an embarrassment for physics. That is about to change.

Neutrinos change their flavor (generation-type) from one flavor to another as they sail through the cosmos. This is phenomenon is called “oscillation”. The 2015 Nobel Prize in Physics went to Takaaki Kajita and Arthur B. McDonald "for the discovery of neutrino oscillations, which shows that neutrinos have mass”. Their work devastatingly refutes claims presented in John Updike’s poem “Cosmic Gall”. Sorry John—while they remain small, neutrinos do have mass after all. Thanks to Kajita and McDonald, not only do we know neutrinos have mass, their work gives us a lower limit on their masses. At least one of the three neutrinos must have a mass bigger than about one-twentieth of an electron volt (physicists use Einstein’s relationship E=mc2 to convert masses to equivalent energies).

This is quite svelte.The next heaviest elementary particle is the electron, whose mass is ten million times larger! Most importantly, these lower limits on neutrino masses give experimentalists thresholds to target. All that’s left is to build a scale sensitive enough to weigh them.

Neutrinos are generated in nuclear reactions such as fusion and radioactive decay. The ultimate reactor, of course, was the biggest cauldron of them all: the Big Bang. Like light, neutrinos are stable. Their lifetimes are infinite because, like light, there is nothing for them to decay into.

Since it’s impossible to collect enough neutrinos to weigh them in a terrestrial laboratory, cosmologists will use massive galaxy clusters as their scales. Sprinkled amidst the luminous matter in the clusters are innumerable neutrinos. Their masses can be measured using gravitational lensing, a direct consequence of Einstein’s General Theory. All matter, dark and luminous, gravitationally deflects light.

The gravitational lensing effect rearranges photon trajectories, as Eddington showed during the 1919 total Solar eclipse. Star positions were displaced from where they would’ve been seen in the absence of the Sun’s warping of space-time. The light that should’ve been there was lensed; the amount of movement told us the mass of lens.

What kind of light should we use to weigh poltergeist particles like neutrinos? There certainly aren’t enough neutrinos in our Solar System to bend the Sun’s light. The most promising light source of all is also the oldest and most abundant light in the universe: the “3 Kelvin” Cosmic Microwave Background (CMB). These cosmic photons arose from the same ancient cauldron that produced the neutrinos plying the universe today. The CMB is “cosmic wallpaper”, a background against which the mass of all matter in the foreground galaxy clusters, including neutrinos, can be measured.

In 2015 the Planck satellite showed powerful evidence for gravitational lensing of the CMB using a technique that is eventually guaranteed to detect neutrino masses. This technique, based on the CMB’s polarization properties, will dramatically improve in 2016 thanks to a suite of experiments deploying tens of thousands of detectors cooled below 0.3 Kelvin at the South Pole and in the Chilean Atacama desert.

Neutrinos are also the very paradigm of Dark Matter: they’re massive, they’re dark (they only interact with light via gravity) and they are neutral; all required properties of Dark Matter. While we know that neutrinos are not the dominant form of the cosmos’ missing mass, they are the only known form of Dark Matter.

After we measure their masses will use neutrinos to thin the herd of potential Dark Matter candidates. Just as there are many different types of ordinary matter, ranging from quarks to atoms, we might expect there are also several kinds of Dark Matter. Perhaps there is a “Dark” Periodic Table.

The hunt is on to directly detect Dark Matter, and several exciting upgrades to liquid noble gas experiments are coming online in 2016. Perhaps there will be detections. But so far the direct detection experiments have only produced upper limits. In the end, neutrinos just might be the only form of Dark Matter we ever get to “see”.

The next century of General Relativity promises to be as exciting as the first. “Spacetime tells matter how to move; matter tells spacetime how to curve” said John Archibald Wheeler. We’ve seen what the curvature is. Now we just need to find out what’s the matter. And where better to look for lost matter than where the Dark is.

In most facets of life, people are perfectly content to let other people act in accordance with their tastes, even when others’ tastes differ from their own. The supertasters of the world, for instance—that 15-or-so percent of us whose tongues are so densely packed with taste buds that they find the flavors of many common foods and drinks too rich or too bitter to enjoy—have at no point in history ever taken to the streets to demand global bans on cabbage or coffee. And the world’s normal tasters, who clearly have a numerical advantage over the supertasters, have never tried to force the supertasters into eating and drinking against their own preferences.

Religion sits at the other end of the “vive la différence” spectrum. All of the world’s major religions, practiced by five out of every seven people on the planet today, teach people to concern themselves with other people’s behavior—and not just the behavior of the people within the religion. Instead, they often teach their adherents to take an interest in outsiders’ behavior as well. Why? Recent scientific work is helping to solve this puzzle—and it has yielded a discovery that Freud would have loved.

At the moment, there are two popular families of theory that seek to explain why religion causes people to praise some behaviors and to condemn others. According to the first of these two lines of theorizing, people espouse religious beliefs—particularly a belief in an all-seeing sky god who watches human behavior and then metes out rewards and punishments (in this life or the next)—because it motivates them (and others) to be more trusting, generous, and honest than they otherwise would be.

But a newer line of theorizing called reproductive religiosity theory proposes that religious morality is not fundamentally about encouraging cooperation. Instead, people primarily use religion to make their social worlds more conducive to their own preferred approaches to sex, marriage, and reproduction. For most of the world’s religions over the past several millennia (which have historically thrived in state societies with agricultural production as the primary economic driver), the preferred sexual strategy has involved monogamy, sexual modesty, and the stigmatization of sex outside of marriage (arguably because it helps to insure paternity certainty, thereby reducing conflict over heritable property such as farm land). Reproductive religiosity theory has a lot to commend it: In a recent cross-cultural study involving over 16,000 participants from fifty-six different nations, researchers found that religious young people (from every region of the world and every conceivable religious background) were more averse to casual and promiscuous sex than were their less religious counterparts. (Tellingly, in most regions, religion also appeared to regulate sexuality more strongly for women than for men.)

Both theories predict that strongly religious people will espouse stricter moral standards than less religious people will—and virtually every survey ever conducted supports this prediction. Religious belief seemingly influences people’s views on topics as varied as government spending, immigration, social inequality, the death penalty, and euthanasia, not to mention homosexuality, same-sex marriage, abortion, pornography, and the role of women in society, but for most of the issues that are not explicitly related to sex, marriage, and reproduction, religion’s influence appears to be rather slight. For the sex-related issues, however, religion’s apparent influence tends to be much stronger.

Reproductive religiosity theory—positing, as it does, that religion really is mostly about sex—makes an even bolder prediction: After you have statistically accounted for the fact that religious people have stricter sexual morals than less religious people do (for instance, they are more disapproving of homosexuality, sexual infidelity, abortion, premarital sex, and women in the workplace), then highly religious people will appear to care little more about violations involving dishonesty and broken trust (transgressions such as stealing, fare-dodging, tax dodging, and driving under the influence, for example) than non-religious people do.

This bolder prediction has now been supported resoundingly, and not only among Americans, but also in a study involving 300,000 respondents from roughly ninety different countries. Highly religious people from around the world espouse stricter moral attitudes regarding both prosociality and sex, but the religious people’s stern moral attitudes toward honesty-related infractions seem to be, from a statistical point of view, mostly along for the ride. It is sex, marriage, and reproduction—and not trust, honesty, and generosity—that lie at the core of moralization for most practitioners of the world religions.

As I mentioned earlier, Freud would have loved these results, but perhaps we shouldn’t be too surprised that religion’s most potent effects on morality relate to sex, marriage, and reproduction. After all, sex is awfully close to the engine of natural selection, so it is not unlikely that evolution has left us highly motivated to seek out any tool we can—even rhetorical ones of the sort that religion can provide—to make the world more conducive to our own approaches to love and marriage. Even so, the intimate link between religion and sexual morality is a particularly important element of certain recent geopolitical developments, so we need to understand it better than we do now.

Over the past several years, Islamic extremists of the Middle East and Sub-Saharan Africa have been systematically perpetrating sexual atrocities against girls and women, and as they have done so, they have drawn explicitly on the moral support of their religious traditions. Make no mistake: War rape is nothing new, all of it is appalling, and none of it is acceptable. But to understand what is happening right now—at a time when Boko Haram fighters capture and then seek to impregnate hundreds of Nigerian schoolgirls, at a time when ISIS fighters capture thousands of Yazidi girls and women and then consign them to lives of unceasing sexual terror—we need to figure out how sets of religious beliefs that are ordinarily bolstered to support monogamy and seemingly bourgeois “family values” can transform gang rape and sexual slavery into religious obligations, not mention the perquisites of having God on your side.

Europeans, as it turns out, are the fusion of three peoples—blue-eyed, dark-skinned Mesolithic hunter-gatherers, Anatolian farmers, and Indo-Europeans from southern Russia. The first farmers largely replaced the hunters (with some admixture) all over Europe, so that six thousand years ago, populations from Greece to Ireland were genetically similar to modern Sardinians—dark-haired, dark-eyed, light-skinned. They probably all spoke related languages, of which Basque is the only survivor.

About five thousand years ago, Indo-Europeans arrived out of the East, raising hell and cattle. At least some of them were probably blond or red-headed. In northern Europe they replaced those first farmers, root and branch. Germany had been dotted with small villages before their arrival—immediately afterwards, no buildings. Mitochondrial variants carried by 1 in 4 of those first farmers are carried by 1 in 400 Europeans today, and the then-dominant Y-chromosomes are now found at the few percent level on islands and mountain valleys—refugia. It couldn’t have been pretty.

In southern Europe, the Indo-Europeans conquered and imposed their languages, but without exterminating the locals—even today southern Europeans are mostly descended from those early farmers.

In other words, the linguists were correct. For a while the archaeologists were too: V. Gordon Childe laid out the right general picture (The Aryans: A Study of Indo-European Origins) back in 1926. But then progress happened: vast improvement in archaeological techniques, such as C-14 dating, were accompanied by vast decreases in common sense. Movements of whole peoples—invasions and Völkerwanderungs—became “problematic,” unfashionable: they bothered archaeologists, and therefore must not have happened. Sound familiar?

The picture is clear now due to investigations of ancient DNA. We can see if populations are related, or not; if they fused, or if one replaced another, and to what extent. We even know that one group of ancient Siberians contributed to both Indo-Europeans and Amerindians.

We also know that modern social scientists are getting better and better at coming to false conclusions. You could blame the inherent difficulty of a historical science like archaeology, where experiments are impossible. You might blame well-funded STEM disciplines for drawing away many of the sharper students. You could blame ideological uniformity—but you would be mistaken. Time travelers bringing back digitally authenticated full-color 3D movies of prehistory wouldn't fix this problem.

A defining feature of science is its capacity to evolve in response to new developments. Historically—changes in technological capacities, quantitative procedures, and scientific understanding have all contributed to large-scale revisions in the conduct of scientific investigations. Pressure is mounting for further improvements. In disciplines such as medicine, psychology, genetics, and biology researchers have been confronting findings that are not as robust as they initially appeared. Such shrinking effects raise questions not only about the specific findings they challenge, but more generally about the confidence that we can have in published results that have yet to be re-evaluated.

In attempting to understand its own limitations, science is fueling the consolidation of an emerging new discipline: meta-science. Meta-science, the science of science, attempts to use quantifiable scientific methodologies to elucidate how current scientific practices influence the veracity of scientific conclusions. This nascent endeavor is joining the agendas of a variety of fields including medicine, biology, and psychology—each seeking to understand why some initial findings fail to fully replicate. Meta-science has its roots in the philosophy of science and the study of scientific methods, but is distinguished from the former by its reliance on quantitative analysis and from the latter by its broad focus on the general factors that contribute to the limitations and successes of scientific investigations.

This year the most ambitious meta-scientific study to date was published in Science by Brian Nosek and the Open Science Collaboration. A large-scale effort in psychology sought to replicate 100 “quasi-randomly” selected studies from three premier journals and found that less than half (39 percent) of the studies reached traditional levels of significance when replicated. This study is noteworthy because it directed the lens of science not at any particular phenomena but rather at the process of science itself. In this sense, it represents one of the first major implementations of evidence-based meta-science. Although it is certain to have a major impact on science, only time will tell how it will be remembered.

Although I am enthusiastic about the meta-scientific goals that this study exemplifies, I worry that major limitations in its design and implementation may have produced a misleadingly pessimistic assessment of the health of the field of psychology. Numerous factors may have contributed to an underestimation of the reliability of the findings, including: variations in the skills and motivations of the replicating scientists, limitations in the statistical power of the replications, and perhaps most importantly, questions regarding the fidelity with which the original methods were reproduced. Although the authors attempted to vet their replication procedure with the originating lab, many of the replicated studies were conducted without the originating lab’s endorsement, and these unapproved efforts disproportionately contributed to the low replication estimate.

Even the studies that used procedures that were approved by the originating laboratories still may have been lacking in fidelity. For example, one of the more well-known findings that failed to replicate involved the observation that exposing people to an anti free will message can increase cheating. I am particularly familiar with this example, (and perhaps biased to defend it) as I was a co-author of the original study. Although we signed off on the replication protocol, we subsequently discovered a small but important detail that was left out of the replicating procedure. In the original study, but not the replication, the anti free will message was framed as part of an entirely different study. We have recently found that people are less likely to change their beliefs about free will when the anti free will message is introduced as part of the same study. Apparently people are reluctant to change their mind on this important topic if they feel coerced to do so. In this context it is notable that in the replication study, the anti free will message failed to significantly discourage participants from believing in free will in the first place, and thus could hardly have been expected to produce the further ramification of increased cheating. I suspect that a big portion of failures to replicate may involve the omission of similar small but important methodological details.

As the emerging field of meta-science moves forward, it will be important to refine techniques for understanding how disparities between original studies and replications may contribute to difficulties in reproducing results. Increasing the transparency of originally conducted studies, through methods such as detailed pre-registration, is likely to make it easier for replication teams to understand precisely how the project was originally implemented. However, it will also be important to develop methods for evaluating the fidelity of the reproductions themselves.

Another important next step for meta-science is the implementation of prospective replication experiments that systematically investigate how new hypotheses fair when tested repeatedly across laboratories. Prospective replication experiments will help to overcome potential biases inherent in selecting which published studies to replicate while simultaneously illuminating various factors that may govern the replicability of scientific findings, including variations in population sample, researcher investment and reproduction fidelity.

More generally, as we adopt a more meta-scientific perspective, researchers will hopefully increasingly appreciate that just as a single study cannot irrefutably demonstrate the existence of a phenomenon, neither can a single failure to replicate disprove it. Over time, scientists will likely become increasingly comfortable with meticulously documenting and (ideally) pre-registering all aspects of their research. They will see the replication of their work not as a threat to their integrity but rather as testament to their work’s importance. They will recognize that replicating other findings is an important component of their scientific responsibilities. They will refine replication procedures to not only discern the robustness of findings, but to understand their boundary conditions, and the reasons why they sometimes (often?) decline in magnitude. Even if history discerns that the original foray into meta-science was significantly lacking, ultimately meta-science will surely offer deep insights into the nature of the scientific method itself.

Sometimes news creeps up on us slowly. The discovery of the electron by J.J. Thomson in 1897 marked the first step in constructing the Standard Model of Particle Physics, an endeavor that culminated in the discovery of the Higgs boson in 2012. The Standard Model is a boring name for a breathtaking theory, describing quarks, leptons, and the bosons that hold them all together to make material objects. Together with gravity, captured by Einstein's general theory of relativity, we have what Nobel Laureate Frank Wilczek has dubbed the Core Theory: a complete description of all the particles and forces that make up you and me, as well as the sun, moon, and stars, and everything we've directly seen in every experiment performed here on Earth.

There is a lot we don't understand in physics: the nature of dark matter and dark energy, what happens at the Big Bang or inside a black hole, why the particles and forces have the characteristics they do. We certainly don't know even a fraction of what there is to learn about how the elementary particles and forces come together to make complex structures, from molecules to nation-states. But there are some things we do know—and that includes the identity and behavior of all of the pieces underlying the world of our everyday experience.

Could there be particles and forces we haven't yet discovered? Of course—there almost certainly are. But the rules of quantum field theory assure us that, if new particles and forces interacted strongly enough with the ones we know about to play any role in the behavior of the everyday world, we would have been able to produce them in experiments. We've looked, and they're not there. Any new particles must be too heavy to be created, or too short-lived to be detected; any new forces must be too short-range to be noticed, or too feeble to push around the particles we see. Particle physics is nowhere near complete, but future discoveries in that field won't play a role in understanding human beings or their environment.

We'll continue to push deeper. There's a very good chance that "particles and forces moving through spacetime" isn't the most fundamental way of thinking about the universe. Just as we realized in the 19th century that air and water are fluids made of atoms and molecules, we could discover that there is a layer of reality more comprehensive than anything we currently imagine. But air and water didn't stop being fluids just because we discovered atoms and molecules; we still give weather reports in terms of temperature and pressure and wind speed, not by listing what each individual molecule in the atmosphere is doing. Similarly, a thousand and a million years from now we'll still find the concepts of the Core Theory to be a useful way of talking about what we're made of.

Could we be wrong in thinking that the Core Theory describes all of the particles and forces that go into making human beings and their environments? Sure, we could always be wrong. The Sun might not rise tomorrow, we could be brains living in vats, or the universe could have been created last Thursday. Science is an empirical enterprise, and we should always be willing to change our minds when new evidence comes in. But quantum field theory is a special kind of framework. It's the unique way of accommodating the requirements of quantum mechanics, relativity, and locality. Finding that it was violated in our everyday world would be one of the most surprising discoveries in the history of science. It could happen—but the smart money is against it.

The discovery of the Higgs boson at the Large Hadron Collider in 2012 verified that the basic structure of the Core Theory is consistent and correct. It stands as one of the greatest accomplishments in human intellectual history. We know the basic building blocks of which we are made. Figuring out how those simple pieces work together to create our complex world will be the work of many generations to come.

The brain is a strange planet; the planet is a strange brain. The most important science story of the past year isn’t one story, but an accumulation of headlines. With advanced recording and communication technologies, more people than ever before are sharing details of individual experiences and events, making culture more permeable and fluid. This verifiable record of diversity has brought people together while also amplifying their differences.

Marginalized groups are enjoying widespread recognition of their rights. Measures like federal legalization of gay marriage and partial, state-level legalization of marijuana show lifestyles once considered abnormal have gained acceptance. At the same time, political divisions remain alarmingly stark. In the past year, over one million fled their home countries for Europe, seeking a better life. And not all signs show we are becoming more tolerant. One of the frontrunners for the Republican presidential nomination suggested America address the threat of global terror by denying all Muslims entry into the United States. He performs well in the polls.

At the summit on climate change in Paris in December, delegations from India and China objected to measures that would limit their economic development by curtailing their ability to pollute. Without recognizing that today’s climate is a product of damaging methods of expansion, India and China argue emerging markets should be able to destroy the environment as did Western economies as they matured. This perspective is driven by fear of being usurped, outpaced and overcome, and its myopia demonstrates fear short-circuits the logical decision-making of entire countries just as it does that of individuals.

“One person’s freedom ends where another’s begins,” we seem to be saying. If this is what drives the formation of cultural norms today, dialogue about issues like water rights, energy use and climate change will be determined by how these issues actually affect individuals.

According to F. Scott Fitzgerald, “The test of a first-rate intelligence is the ability to hold two opposed ideas in the mind at the same time and still retain the ability to function.” Our culture has potential to realize a seismic shift in consciousness and rebalance environmental and social scales. We’ve invented the story of this world—the cities we live in, the language and symbols we use to articulate thoughts, the love we nurture to propel us forward. A shift in perspective may be all it takes to convince us the greatest threat to humanity comes not from another people in another land, but from all people, everywhere.

One of the most profound puzzles in modern physics is to describe the quantum nature of spacetime. A real challenge here is that of finding helpful experimental guidance. Interestingly, we are just now on the verge of gaining key new experimental information about classical spacetime, in new and important regimes—and this tantalizingly also offers a possibility of learning about quantum spacetime as well.

Of course, the community has been abuzz about the possible discovery of a new particle at LHC, seen by its disintegration into pairs of photons. If this is real—and not just a fluctuation—there’s a slim chance it is even a graviton in extra dimensions, which, if true, could well be the discovery of the century. While this would indeed be a probe of quantum spacetime, I’ll put this aside until more data reveals what is happening at LHC.

But on the long-distance front, we are clearly entering a new era in several respects. First, miles long instruments built to detect gravitational waves have just reached a sensitivity where they should be able to see these spacetime ripples, emitted from collisions and mergers of distant black holes and neutron stars. In fact, there have been very recent hints of signals seen in these detectors, though the physics community eagerly awaits a verifiable signal. Once found, these will confirm a major prediction of Einstein’s general relativity, and open a new branch of astronomy, where distant objects are studied by the gravitational waves they emit. There is also the possibility that precise measurements of the microwave radiation left over from the Big Bang will reveal gravity waves, though the community has backpedaled from the premature announcement of this in 2014, and so the race may well be won by the earth-based gravity-wave detectors. Either way, these developments will be very exciting to watch.

The possibility of even more profound tests of general relativity exists with another new “instrument,” the Event Horizon Telescope, which is being brought on line to study the four million solar mass black hole at the center of our Milky Way galaxy. Instrument is in quotes because the EHT is really a network of radio telescopes, which combine to make a telescope the size of planet Earth. This will offer an unprecedentedly sharp focus on both our central black hole, and on the six billion solar mass black hole at the center of the nearby elliptical galaxy M87. In fact, with the telescopes that have been networked so far, we are beginning to see structure whose size is close to that of the event horizon of our central black hole. EHT should ultimately probe gravity in a regime where it gets extremely strong—so strong that the velocity that objects need to escape its pull is approaching the speed of light. This will give us a new view on gravity in a regime where it has so far not been well tested.

Even more tantalizing is the possibility that EHT will start to see effects that begin to reveal a more basic quantum reality underlying spacetime. For the 2014 Edge Question, I wrote that apparently our fundamental concept of spacetime is ready for retirement, and it needs to be replaced by a more basic quantum structure. There are multiple reasons for this. One very good one is the crisis arising from the attempt to explain black hole evolution within present day physics; our current foundational principles, including the idea of spacetime, come into sharp conflict with each other when describing black holes. While Stephen Hawking initially predicted that quantum mechanics must break down when we account for emission of particles of “Hawking radiation” from a black hole, there are now good indications that it should not be abandoned. And if quantum mechanics is to be saved, this tells us that quantum information must be able to escape a black hole as it radiates particles—and this confronts our understanding of spacetime.

The need for information to escape an evaporating black hole conflicts with our current notions of how fields and particles move in spacetime; here, escape is forbidden by the prohibition of faster-than-light travel. A key question is how the familiar spacetime picture of a black hole must be modified to allow such escape. The modifications must apparently extend out at least to the event horizon of a black hole. Some have postulated that the new effects abruptly stop right there, at the horizon, but this abruptness is rather unnatural, and leads to other apparently crazy conclusions associated with what has been recently renamed the “firewall” scenario. A more natural scenario, though, is that the usual spacetime description is also modified in a region that extends outward beyond the black hole horizon, at least through the region where gravity is very strong; the size of this region is perhaps a few times the horizon radius. So, in short, the need to save quantum mechanics indicates quantum modifications to our current spacetime description that extend into the region that EHT observations will be probing! Important problems are to improve understanding of the nature of these alterations to the familiar spacetime picture, and to determine more carefully their possible observability via EHT’s measurements.

Although we have been talking about the microbiome for years, news about our microbial friends was huge this year.

We have known for some time that the microbes in our gut were extremely important for our health, but recently studies are beginning to show that the gut biome is even more important than we previously imagined.

Fecal Microbiota Transplantation, or FMTs, have been shown to cure Clostridium difficile infections in 90 percent of cases, a condition notoriously difficult to treat any other way. We don’t know exactly how FMTs work, other than that the introduction of microbiota (poop) from a healthy individual somehow causes the gut of an afflicted patient to regain its microbial diversity and rein in the rampant Clostridium difficile.

It appears that our gut microbes produce a wide variety of neurotransmitters that influence our brains, and vice versa, much more than previously believed. There is evidence that, in addition to mood, a number of brain disorders may be caused by microbial imbalance. The evidence is so strong that FMT banks such as OpenBiome have started screening donors for psychiatric problems in addition to a wide variety of health issues. Consequently, it is now harder to qualify as a donor to a fecal bank than it is to get into MIT or Harvard. Perhaps machines can help us here as they do everywhere else; Robogut is making headway in creating synthetic poop.

It has been shown that mice without gut microbes socialize less than mice with proper gut biomes, causing scientists to theorize that while socialization doesn’t help the fitness of mice, their social behavior and their habit of eating each other’s feces may be driven by the microbes “wanting” to be shared between the mice.

There is evidence that many of our favorite foods are really the favorite foods of our gut microbes, which turn those foods into things that our bodies need and like. Also, it appears that oligosaccharides which are abundant in breast milk and which are regarded to be metabolically "inert" to us selectively feed some of our “good” gut microbes. Not only are the microbes more abundant in the human body than human cells, it appears that they may be the reason we do many of the things that we do and are as important, if not more important, in many of the processes that occur in our body than our own cells.

However, not all microbes are good for us. In fact, most microbes are “neutral” and some are bad from the perspective of desirable health outcomes for us, the hosts. Take, for example, Toxoplasma gondii, which causes infected rats to lose their natural fear of cats because Toxoplasma gondii requires cats to reproduce sexually. Or rabies that causes animals to attack other animals to increase transmission. It could be more than just our mood that is controlled by microbes.

And the microbes are everywhere. The detergents that we use have eliminated the ammonia-oxidizing bacteria (AOB) on our skin—a bacterial that is present on the skin of all Yanomami, the indigenous people in the Amazon rainforest. It turns out that pre moser hygiene tribes like the Yanomami, among other things, do not suffer from acne or most forms of inflammatory skin diseases. In a study of over a thousand of members of the Kitavan Islanders of Papua New Guinea, there was not a single case. There is increasing evidence that allergies and many modern ailments have come into existence only after the invention of modern hygiene.

The microbes in the air are also part of the system. There are studies showing that infection rates in hospitals decrease if you open a window and let the diverse outdoor microbes in compared to aggressive systems that filter and sterilize the air.

The microbes in the soil appear to be an essential part of the system to produce the nutrients for our plants and the microbes in the plants are an essential part of how the plants convert the nutrients into flavors and nutrients for us. Generations of using artificial fertilizers, destroying the microbial flora of our soil and then later “enriching” our blank calories with the over-simplistic vitamins that happen to be the molecule de jour may have been exactly the opposite of what we should be doing.

The human gut, particularly the colon, has the highest recorded microbial density of any known microbial habitat. Our gut is almost the perfect environment to support the biodiversity and complexity that is our gut biome. The temperatures are well regulated, with us, the human host, able to survive in extreme conditions. The host can travel to extreme distances though their lifetime transporting and sharing microbes with these environments. From the perspective of the microbes, we are an almost perfectly evolved life support system for them. Maybe it’s arrogant to think about the microbes as some sort of “little helpers” in our system, but maybe it’s more accurate to think of us as architectural innovations by the microbes.

As we understand more and more about the genome, the epigenome, the brain, and the variety of complex systems that make us what we are, and the more I learn about the microbiome, the more it feels like maybe modern medicine is like the proverbial aliens trying to understand human motivation by only looking at the cars on the freeway through a telescope and that we have a long way to go before we will really understand what’s going on.

Every year, more women than men become college-educated. The disparity is already prevalent across North America and Europe, and the trend is beginning to spread across the world more widely. At the University of Texas at Austin where I teach, the sex ratio is 54 percent women to 46 percent men. This imbalance may not seem large at first blush. But when you do the math it translates into a hefty 17 percent more women than men in the local mating pool. Speculations about reasons range widely. They include the gradual removal of gender discrimination barriers and women’s higher levels of conscientiousness (relative to men’s) that translate into better grades and superior college app qualifications. Whatever the causes turn out to be, the disparity is creating a dramatic and unintended mating crisis among educated women.

We must look deeply into our mating psychology to understand the far-reaching consequences of the sex ratio imbalance. Women and men both have evolved multiple mating strategies; some of each gender pursue casual hook-ups, some committed partnerships. Some alternate at different times of their lives, and some do both simultaneously. And although a few social scientists deny the data, research overwhelmingly shows that men harbor, on average, a greater desire for sexual partner variety. Men experience more frequent sexual thoughts per day, have more sexual fantasies involving multiple partners, and more readily sign up for online dating sites for the sole goal of casual sex. So, a surplus of women among educated groups caters precisely to this dimension of men’s sexual desires because the rarer gender is always better positioned to get what they want on the mating market. In places like large cities in China, with their surplus of men, women can better fulfill their desires while many men remain frustrated and mateless. Context matters. For every surplus of women in places like Manhattan, there exist pockets where men outnumber women, such as schools of engineering or the software companies of Silicon Valley. But when there are not enough men to go around, women predictably intensify their sexual competition. The rise of the hookup cultures on college campuses and online dating sites like Tinder, Adult Friend Finder, and Ashley Madison is no coincidence.

Gender differences in sexual psychology are only part of the problem. Additional elements of the mating mind exacerbate it. A key cause stems from the qualities women seek in committed mateships. Most women are unwilling to settle for men who are less educated, less intelligent, and less professionally successful than they are. The flip side is that men are less exacting on precisely these dimensions, choosing to prioritize, for better or worse, other evolved criteria such as youth and appearance. So the initial sex ratio imbalance among educated groups gets worse for high achieving women. They end up being forced to compete for the limited pool of educated men not just with their more numerous educated rivals, but also with less educated women whom men find desirable on other dimensions.

The depletion of educated men worsens when we add the impacts of age and divorce to the mating matrix. As men age, they desire women who are increasingly younger than they are. Intelligent, educated women may go for a less accomplished partner for a casual fling, but for a committed partner they typically want mates their own age or a few years older, and at least as educated and career-driven. Since education takes time, the sex ratio imbalance gets especially skewed among the highly educated—those who seek advanced degrees to become doctors, lawyers, or professors, or who climb the corporate ladder post-MBA. And because men are more likely than women to remarry following divorce and to marry women increasingly younger than they are—three years at first marriage, five at second, eight at third—the gender-biased mating ratio skews more sharply with increasing age.

Different women react in different ways to the mating crisis. Some use sexual tactics to ramp up their competition for men. They dress more provocatively, send more sexually explicit texts, consent to sex sooner, and hope that things turn into something more than a brief encounter. Some women opt out of the mating game by choice because they are unwilling to compromise their careers in the service of mating. Although some progress has been made, it is still true that women suffer disproportionately from compromises between career and family. And some women hold out for an ever-smaller pool of men who are single, educated, and emotionally stable, who are not sexual players, and who can engage their intellect, sense of humor, emotional complexities, and sexual passions for more than just a night.

The good news for those who succeed is that marriages among the educated tend to be more stable, freer of conflict, less plagued by infidelity, and less likely to end in divorce. Educated couples enjoy a higher standard of living as dual professional incomes catapult them to the more affluent tiers of the economic strata. They suffer less financial stress than their less educated counterparts. Assortative coupling on education level does have an unintended down side—it’s a major contributor to economic inequality in the larger society, widening the gap between the haves and have-nots. But for accomplished women who successfully traverse the waters of a mating pool unfairly stacked against them, mating triumph at the individual level typically takes precedence over loftier goals of reducing societal-level inequality when the two come into conflict.

What are the potential solutions to the mating pool shortage for educated women? Adjust their mate preferences? Expand the range of men they are willing to consider as mates? Mating psychology may not be that malleable. The same mating desires responsible for the skewed gender imbalance to begin with continue to create unfortunate obstacles to human happiness. As successful women overcome barriers in the workplace, they encounter new dilemmas in the mating market.

On July 14 of this year, NASA’s New Horizons spacecraft flew within 7,800 miles of Pluto, after traversing 3 billion miles since its 2006 launch, and it began sending back astonishing and detailed images of mountains and plains composed of ice from the last planet in our system to be explored. But, for me, this was not the most newsworthy aspect of its mission.

The science involved in accomplishing this feat is amazing. New Horizons is an engineering marvel, with radioisotope power generation, sophisticated batteries, optical and plasma scientific instruments, complex navigation and telemetry, and so on. Its primary missions—all successfully completed—were to map the surface of Pluto and its main moon, Charon; to characterize the geology and composition of these bodies; and to analyze the atmosphere of these bodies. In the process, New Horizons has also shed light on the formation of our solar system.

It took an army of scientists and engineers working for over a decade to design, build, and manage the probe. Of course all these people, and many others around the world, took enormous pleasure and satisfaction, and experienced tremendous wonder, in seeing the resulting photographs and data from so far away.

We succeed in this kind of exploration of our solar system so reliably nowadays that this sort of satisfaction seems to us routine, feels like just a bump in the road of our endless inquiry. But the exploration of Pluto is, alas, just a bump in the road in another, rather more dispiriting way.

Most Americans (58 percent in a Pew survey conducted in 2011) are supportive of space exploration, valuing both its contributions to science and to national pride. And most Americans (59 percent) think that sending astronauts into space is desirable (in a 2015 Pew survey). Yet, Americans and their politicians appear unwilling to spend more money on this; results from a 2014 General Social Survey indicate that just 23 percent of Americans think we should spend more. By contrast, 70 percent of Americans think we should spend more on education and 57 percent think we should spend more on health. NASA’s budget has been roughly constant in real dollars since 1985 (it is now 0.5 percent of the federal budget), but it is well below its peak in 1965, when it was over 4 percent of the federal budget (and it’s below even the 1 percent level of 1990).

The captivating photos of Pluto occupied a week or so in the news cycle in the middle of the summer, and we’ve moved on. What really amazes me about the news from Pluto is that there are not more people who find this accomplishment astonishing, that there is not even more sustained support for space exploration. NASA’s entirely sensible push to make both manned and unmanned space exploration reliable, standardized, and safe has also had the unfortunate side effect of making it routine and even boring for many people. For me, this fact— brought into relief once again by the mapping of Pluto—is the real newsworthy part of this discovery.

My paternal grandfather, who was born in Greece in the 1890s (and was orphaned not long after), used to tell me that he simply could not believe that he had heard about the first heavier-than-air flight of the Wright brothers when it happened in 1903 and that he had also watched the moon landing in 1969. This was the same man who fought in World War I and who would tell me stories about how his unit was transferred from Ankara to Kiev on horseback “when the Bolsheviks revolted” and about how, during World War II, he kept his family alive in Athens “when the Nazis invaded.” But space exploration interested him more because, well, it was so much more optimistic. Humans went from riding on a beach with a plane made of canvas and bicycle parts to traversing space with a lunar lander in sixty-six years. It amazed him, having witnessed it first hand—and the pace and sheer wonder astonish me even as I write this.

I realize of course that the great accomplishments in space exploration of the 1960s and 70s were largely motivated by the Cold War. I realize as well that many people are now arguing that private enterprise should take over space exploration. And I know that commitment to space exploration is low because many see better uses for our money. Is it better to vaccinate children, care for the poor, or invest in public health or medical research, rather than invest in space exploration? Part of my response is the customary one that science and discovery are the ultimate drivers of our wealth and security. But my main response is that this is a false dichotomy. The real question is whether we would rather wage war or colonize Mars. Which would be, and should be, more newsworthy? In this, I think my grandfather had it right.

We’ve known about “quantum weirdness” for more than 100 years, but it’s still making headlines. In the summer of 2015, experimental groups in Boulder, Delft, and Vienna announced that they had completed a decades-long quest to demonstrate quantum nonlocality. The possibility of such nonlocal effects first captured the attention of physicists in the 1930s, when Einstein called it, “spooky action at a distance”—indicating that he perceived it as a bug of the nascent theory. But on this particular issue, Einstein couldn’t have been more wrong: nonlocality isn’t a bug of quantum mechanics, it’s a pervasive feature of the physical world.

To understand why the scientific community has been so slow to embrace quantum nonlocality, recall that 19th century physics was built around the ideal of local causality. According to this ideal, for one event to cause another, those two events must be connected by a chain of spatially contiguous events. In other words, for one thing to have an effect on another, the first thing needs to touch something, which touches something else, which touches something else … eventually touching the other thing.

For those of us schooled in classical physics, the notion of local causality might seem central to a rational outlook on the physical world. For example, I don’t take reports of telekinesis seriously—and not because I’ve taken the time to examine all the experiments that have tried to confirm its existence. No, I don’t take reports of telekinesis seriously because it seems irrational to believe in some sort of causality that doesn’t involve things moving through space and time.

But QM appears to conflict with local causality. According to QM, if two particles are in an entangled state, then the outcomes of measurements on the second particle will always be strictly correlated (or anticorrelated) with measurements on the first particle—even when the second particle is far, far away from the first. Quantum mechanics also claims that neither the first nor the second particle has any definite state before the measurements are performed. So what explains the correlations between the measurement outcomes?

It’s tempting to think that quantum mechanics is just wrong when it says that the particles aren’t in any definite state before they are measured. In fact, that’s exactly what Einstein suggested in the famous “EPR” paper with Podolsky and Rosen. However, in the 1960s, John Bell showed that the suggestion of EPR could be put to experimental test. If, as suggested by Einstein, each particle has its own state, then the results of a certain crucial experiment would disagree with the predictions made by quantum mechanics. Thus, in the 1970s and 1980s, the race was on to perform this crucial experiment—an experiment that would establish the existence of quantum nonlocality.

The experiments of the 1970s and 80s came out decisively in favor of quantum nonlocality. However, these experiments left open a couple of loopholes. It was only in 2015 that the ingenious experimenters in Boulder, Delft, and Vienna were able to definitively close these loopholes—propelling quantum nonlocality back into the headlines.

But is it news that quantum mechanics is true? Didn’t we already know this, or at least, wasn’t the presumption strongly in its favor? Yes, the real news here isn’t that quantum mechanics is true. The real news is that we are learning how to harness the resources of a quantum world. In the 1920s and 30s, quantum nonlocality was a subject of philosophical perplexity and debate. In 2015, questions about the meaning of quantum nonlocality are being replaced by questions about what we can do with it. For instance, quantum nonlocality could facilitate information-theoretic and crytographic protocols that far exceed anything that could have been imagined in a world governed by classical physics. And this is the reason why quantum nonlocality is still making headlines.

But don’t get carried away—quantum nonlocality still doesn’t make it rational to believe in telekinesis.

What is morality and where does it come from? Why does it exert such a tremendous hold over us? Scholars have struggled with these questions for millennia, and for many people the nature of morality is so baffling that they assume it must have a supernatural origin. But the good news is that we now have a scientific answer to these questions.

Morality is made of meat. It is a collection of biological and cultural solutions to the problems of cooperation recurrent in human social life. Which problems? Caring for families, working in teams, trading favors, resolving conflicts. Which solutions? Love, loyalty, reciprocity, respect. The solutions arose first as instincts, designed by natural selection; later, they were augmented and extended by human ingenuity, and transmitted as culture. These mechanisms motivate social, cooperative, and altruistic behavior; and they provide the criteria by which we evaluate the behavior of others. And why is morality felt to be so important? Because, for a social species like us, the benefits of cooperation (and the opportunity costs of its absence) can hardly be overstated.

The scientific approach was news when Aristotle first hypothesized that morality was a combination of the natural, the habitual, and the conventional—all of which helped us to fulfill our potential as social animals. It was news when Hobbes theorized that morality was an invention designed to corral selfish individuals into mutually-beneficial cooperation. It was news when Hume proposed that morality was the product of animal passions and human artifice, aimed at the promotion of the “publick interest.” It was news when Darwin conjectured that “the so-called moral sense is aboriginally derived from the social instincts,” which tell us how “to act for the public good.” And it has been front-page news for the past few decades, as modern science has made discovery after discovery into the empirical basis of morality, delivering evolutionary explanations, animal antecedents, psychological mechanisms, behavioral manifestations and cultural expressions.

Unfortunately, many philosophers, theologians, and politicians have yet to get the message. They make out that morality is still mysterious, that without God there is no morality, and that the irreligious are unfit for office. This creationist account of morality— “good of the gaps”—is mistaken, and alarmist.

Morality is natural, not supernatural. We are good because we want to be, and because we are sensitive to the opinions—the praise and the punishment—of others. We can work out for ourselves how best to promote the common good, and with the help of science, make the world a better place.

The variously attributed Platinum Rule holds that we should do unto others as they would have us do unto them. The most important news is that there is growing evidence that every endeavor involving social connection—friendship and marriage, education, health care, organizational leadership, interracial relationships and international aid, to name a few—is more effective to the extent that it adheres to this behavioral guide. The reason that the beneficial consequences of holding to the rule will remain important news is that the Platinum Rule is not simple and hewing to it is tough, especially in an individualist culture that fosters the wisdom of one’s own take on reality. Following the dictates of the Platinum Rule is so tough, in fact, that we routinely ignore it and then find it surprising and newsworthy when a new study discovers its truth all over again.

The challenge of holding to the Platinum Rule begins with the realization that it is not the Golden Rule—do unto others as you would have them do unto you. The Golden Rule is also a good behavioral guide and one that shows up across the religious traditions (e.g., in Judaism—what is hateful to you, do not do to your neighbor; in Confucianism—do not do to others what you do not want done to yourself). Yet built into the very foundation of the Golden Rule is that the assumption that what is good, desirable, just, respectful, and helpful for ME will also be good, desirable, just, respectful and helpful for YOU (or should be, and even it isn’t right now, trust me, it will be eventually).

Even with good friends or partners this is often not the case. For example, from your perspective you may be certain you are giving me support and fixing my problem. Yet what I would prefer and find supportive and have you do unto me is for you to listen to me and my analysis of the problem. And in the many cases in which we now strive to connect with people across social class, sexual orientation, race, ethnicity, religion, and region of the world, some disconnect between how you think you should treat people and how they would like to be treated is almost certainly the case. Doing unto others as they would have you do unto them requires knowledge of others, their history and circumstances, what matters to them, appreciating and acknowledging the value of difference, and accommodating one’s actions accordingly.

At the base of the successful application of the Platinum Rule is the realization that one’s own way is one way and may not be the only or the best way. Yes, not all ways are good; some are uninformed, corrupt and evil. Yet the findings from cultural science are increasingly robust. There is more than one good or right or moral way to raise a child, educate a student, cope with adversity, motivate a workforce, develop an economy, build a democracy, be healthy and experience well-being.

For many of us, what is good for me and what I assume will be good for you too is likely grounded in what cultural psychologists call a WEIRD perspective, that is, a Western, Educated, Industrialized, Rich and Democratic perspective. For the 75 percent of the world’s population who cannot be so classified, (i.e., the majority of the world, including, for example, many people in North America without a college degree or with non-Western heritage), who I am, what matters to me, what I hope to be, and what I would most like done unto me may not match what seems so obviously and naturally good and appropriate from the WEIRD perspective.

Beyond knowledge of the other and the appreciation of difference, the Platinum Rule requires something even harder—holding my own perspective at bay while thinking and feeling my way into the position of the other and then creating space for this perspective. Such effort requires a confluence of cognitive, affective and motivational forces. Some researchers call this psychological work perspective taking, some empathy, some compassion, still others social or emotional intelligence. Whatever the label, the results are worth the effort.

When colleges ask students from working class or underrepresented minority backgrounds to write about matters to them or to give voice to their worries about not fitting in college, they are happier, healthier, and outperform students not given these opportunities. Managers who encourage employees to reflect on the purpose and meaning of their work have more effective teams than those who don’t. The odds of persuading another in an argument are greater if you acknowledge the opponent’s moral position before asserting your own. Research from across the social sciences supports the idea that just recognizing the views, values, needs, wants, hopes, or fears of others can produce better teaching, medicine, policing, team leadership, and conflict resolution. Taking their views into account may change the world.

Perhaps even more newsworthy than the successes of understanding what matters to others are the many Platinum Rule fails. Government and private donors distributed billions following the 2010 earthquake in Haiti, much of it spent doing what the donors believed they would do if they were in the place of those devastated by the disaster. One notable project was a campaign to underscore the good health consequences of hand washing to people without soap or running water. Many humanitarians now argue that relief efforts would be less costly and more effective if instead of giving people what donors think they need–water, food, first aid, blankets, training—they delivered what the recipients themselves say they need. In most cases, this is money.

Whether independent North Americans who, according to some surveys, are becoming more self-focused by the year can learn the value of the Platinum Rule is an open question. To this point, the science suggests it would be moral, efficient, and wise.

We live at a remarkable moment in history. Our scientific instruments have allowed us to see the far reaches of the cosmos and to study the tiniest particles. In both cases, they have revealed a surprising simplicity, at odds with the most popular theoretical paradigms. I believe this simplicity to be a clue to a new scientific principle, whose discovery will represent the next revolution in physics and in our understanding of the universe.

It is not without irony that at the very moment the observational situation is clearing so beautifully, the theoretical scene has become overwhelmingly confused. Not only are the most popular models very complicated and contrived, they are also being steadily ruled out by the new data. Some physicists appeal to a “multiverse” in which all possible laws of physics are realized somewhere since then, they hope, there would at least be one region like ours. It seems more likely to me that the wonderful new data is pointing us in the opposite direction. The cosmos isn’t wild and unpredictable, it is incredibly regular. In its fundamental aspects, it may be as simple as an atom and, in time, every bit as possible to understand.

Our most powerful-ever microscope, the Large Hadron Collider (LHC), has just found the Higgs boson. This particle is the basic quantum of the Higgs field, a medium which pervades space and endows particles with mass and properties like electric charge. As fundamental to our understanding of particle physics as the Higgs field is, it is equally important to our understanding of cosmology. It makes a big contribution to the energy in empty space, the so-called dark energy, which astronomical observations reveal to be a weirdly tiny, yet positive, number. Furthermore, according to the LHC measurements and the standard model of particle physics, the Higgs field is delicately poised on the threshold of instability in today’s universe.

The discovery of the Higgs boson was a triumph for the theory of quantum fields, the amalgamation of quantum mechanics and relativity which dominated 20th century physics. But quantum field theory has great trouble explaining the mass of the Higgs boson and the energy in empty space. In both cases, the problem is essentially the same. The quantized vibrations of the known fields and particles become wild on small scales, contributing large corrections to the Higgs boson mass and to the dark energy density and generally giving them values much greater than those we observe.

To overcome these problems, many theorists have postulated new particles, whose effects would almost precisely cancel those of all the known particles, “protecting” the mass of the Higgs boson and the value of the dark energy from quantum effects. But the LHC has looked for these extra partner particles and, so far, failed to find them. It seems that nature has found a simpler way to tame quantum phenomena on short distances, in a manner which we have yet to fathom.

Meanwhile, our most powerful-ever telescope, the Planck satellite, has scanned the universe on the largest visible scales. What it has revealed is equally surprising. The whole shebang can be quantified with just six numbers: the age and temperature of the cosmos today; the density of the dark energy and the dark matter (both mysterious, but simple to characterize); and the strength, and slight dependence on scale, of the tiny initial variations in the density of matter from place to place as it emerged from the big bang. None of the complications, like gravitational waves or the more involved density patterns expected in many models, appear to be there. Again, nature has found a simpler way to work than we can currently understand.

The largest scale in physics—the Hubble length—is defined by the dark energy. By accelerating the expansion of the cosmos, the dark energy carries distant matter away from us and sets a limit to what we will ultimately see. The smallest scale in physics is the Planck length, the miniscule wavelength of photons so energetic that two of them will form a black hole. While exploring physics down to the Planck length is beyond the capabilities of any conceivable collider, the universe itself probed this scale in its earliest moments. So the simple structure of the cosmos is likely to be an indication that the laws of physics become simple at this extreme.

All the complexity in the world, including stars, planets and life, apparently resides in the “messy middle.” It is a striking fact that the geometric mean of the Hubble and Planck lengths is the size of a living cell: the scale on which we live, where nature is at her most complex.

What is exciting about this picture is that it requires a new kind of theory, one which is simple at both the smallest and largest scales, and very early and very late cosmological times so that it is capable of explaining these properties of our world. In fact, there are more detailed hints from both theory and data that, at these extremes, the laws of physics should become independent of scale. Such a theory won’t be concerned with kilograms, meters or seconds, only with information and its relations. It will be a unified theory, not only of all the forces and particles but of the universe as a whole.

The most interesting recent physics news is that the Large Hadron Collider (LHC) at the European laboratory CERN, in Geneva, Switzerland, is finally working at its highest ever design energy and intensity. Why that is so important is because it may at last allow the discovery of new particles (superpartners), which would allow scientifically formulating and testing a final theory underlying the physical universe.

As Max Planck immediately recognized when he discovered quantum theory over a century ago, the equations of the final theory should be expressed in terms of universal constants of nature such as Newton’s gravitational constant G, Einstein’s universal speed of light c, and Planck’s constant h. The natural size of a universe is then tiny, about 10-33 cm, and the natural lifetime about 10-43 seconds, far from the sizes of our world. Physicists need to explain why our world is large and old and cold and dark. Quantum theory provides the opportunity to connect the Planck scales with our scales, our world and our physical laws, because in quantum theory, virtual particles of all masses enter the equations and mix scales.

But that only works if the underlying theory is what is called a supersymmetric one, with our familiar particles such as quarks and electrons and force mediating bosons (gluons, and W and Z bosons of the electroweak interactions) each having a superpartner particle (squarks, selectrons, gluinos, etc.). In collisions at LHC the higher energy of the colliding particles turns into the masses of previously unknown particles via Einstein’s E=mc2.

The theory did not tell us how massive the superpartners should be. Naively there were arguments they should not be too heavy (“naturalness”), so they could be searched for with enthusiasm at every higher energy that became accessible, but so far they have not been found. In the past decade or so string- and M-theories have been better understood, and now provide clues to how heavy the superpartners should be. String-theories and M-theories differ technically in ways not important for us here. To be mathematically consistent, and part of a quantum theory of gravity and the other forces, they must have 9 or 10 space dimensions (and one time dimension). To predict superpartner masses, they must be projected onto our world with three space dimensions, and there are known techniques to do that.

The bottom line is that well-motivated string/M-theories do indeed predict that the Large Hadron Collider run (Run II) that started in late 2015, and is planned to move ahead strongly in early 2016, should be able to produce and detect some superpartners, thus opening the door to the Planck scale world, and promoting study of a final theory to testable science. The news that the LHC works at its full energy and intensity, and hopefully can accumulate data for several years, is a strong candidate for the most important scientific news of recent years.

To me, the most interesting bit of news in the last couple of years was the sea-change in attitude among nutritional scientists from an anti-fat, pro-carbohydrate set of dietary recommendations to the promotion of a lower-carbohydrate, selectively pro-fat dietary regime. The issue is important because human health and, indeed, human lives are at stake.

For years, Americans had been told by the experts to avoid fats at all costs and at every opportunity, as if these foodstuffs were the antichrists of nutrition. A diet low in fats and rich in carbohydrates, supposedly, was the way to go in order to achieve a sleek and gazelle-like body and physiological enlightenment. In consequence, no-fat or low-fat foods became all the rage, and for a long time the only kind of yogurt you could find on grocery shelves was the jelly-like zero-fat variety, and the only available canned tuna was packed not in olive oil but in water, as if the poor creature was still swimming.

Unappetizing as much of it was, many Americans duly followed this stringent set of low-fat, high-carbo dietary dos and don’ts. But we did not thereby become a nation of fit, trim, and healthy physical specimens. Far from it. Instead, we became a nation that suffered an obesity epidemic across all age groups, a tidal wave of heart disease, and highly increased rates of Type 2 diabetes. The reason for this was that once they were digested, all those carbohydrate-rich foods got converted into glucose, which raised insulin levels and, in turn, caused storage of excess bodily fat.

Nutritional scientists thus learned the dual lesson that a diet high in carbohydrates can in fact be quite hazardous to your health, and that their alarmist fat phobia was in fact unjustified by the evidence. In reality there are good fats (such as olive oil) and bad fats, healthy carbs and unhealthy carbs (such as refined sugars). As a result, many nutritionists now favor a diametrically opposite approach, allowing certain fats as wholesome and healthy, while calling for a reduction in carbohydrates, especially refined sugars and starches.

A corollary of this about-face in dietary wisdom was the realization that much of so-called nutritional “science” was actually bad science to begin with. Many of the canonical studies of diet and nutrition were flawed by selective use of evidence, unrepresentative sampling, absence of adequate controls, and shifting clinical trial populations. Furthermore, some of the principal investigators were prone to selection bias, and were loath to confront their preconceived viewpoints with contrary evidence. (These and other failings of the discipline are exhaustively documented in journalist Nina Teicholz’s book The Big Fat Surprise [2014].)

Unfortunately, nutritional science still remains something of a backwater. NASA’s Curiosity rover explores the plains, craters, and sand dunes of Mars, and the New Horizons spacecraft takes exquisite pictures of the former planet Pluto. Molecular biologists wield superb gene-editing tools, and are in the process of resurrecting extinct species. Nevertheless, when it comes to the relatively prosaic task of telling us what foods to put in our mouths to achieve good health and to avoid heart disease, obesity, and other ailments, dietary science still has a long way to go.

The use of CRISPR (clustered regularly interspaced short palindromic repeats) technologies for targeted gene editing means that an organism's genome can be cheaply cut and then edited at any location. The implications of such a technology are potentially so great that "crisper" has already become a widely-heard term outside of science, being the darling of radio and television talk shows. And why not? All of a sudden scientists and bio-technologists have a way of making designer organisms. The technology’s first real successes in yeast, fish, flies and even some monkeys have already been widely trumpeted.

But, of course, what is on everyone’s mind is its use in humans. By modifying genes in potential mother’s and father’s egg or sperm cells, they will go on to produce babies "designed" to have some desired trait, or to lack some undesirable trait. Or, by editing genes early enough in embryonic development—a time when only a few cells become the progenitors of all the cells in our bodies—the same design features can be obtained in the adult.

Just imagine, no more Huntington’s, chorea, no more sickle-cell anemia, no more cystic fibrosis, or a raft of other heritable disorders. But what about desirable traits— eye and hair color, personality and temperament, and even intelligence? The first of these—eye and hair color—are already easily within CRISPR’s grasp. The others are probably caused only partially by genes anyway, and even then potentially by scores or possibly hundreds of genes, each exerting a small effect. But who is to say we won’t figure out even these complicated cases one day?

To be sure, the startling progress that genomic and biotechnological workers have made over the last twenty years is not slowing down, and this tells us that there is good reason to believe that, if not in many of our own lifetimes, surely in our children’s, the information of how genes influence many of the traits we would like to design into or out of humans will be widely available.

None of this is lost on the CRISPR community. Already there have been calls for a moratorium on the use of the technology in humans. But the same could have been said in the early days of in vitro fertilization (IVF) technologies, even if those calls didn’t always come from the scientific community. The point is that we have learned that our norms of acceptance to many technological developments get shifted as those technologies become more familiar.

My own view is that the current moratorium on the use of CRISPR technologies in humans won't last long. The technology is remarkably accurate and reliable and this is still very "early days." Refinements to the technologies are inevitable as are demonstrations of its worth in, say, agricultural and environmental issues. The effect of these will be to wear down our resistance to designing humans. Already, CRISPR has been applied successfully to cultured cell lines derived from humans. The first truly and thoroughly designed humans are more than just the subjects of science fiction: they are on our doorsteps, waiting to be allowed in.

The most important news from 2015 in fundamental physics is that probably there is no news. Let me explain. With one tantalizing exception (which may be a statistical anomaly), the experiments done recently confirm a frustratingly incomplete theory of fundamental physics, which has stood since the 1970s. This is in spite of enormous effort by thousands of experimentalists hoping to discover new phenomena that would lead to greater unification and simplification in our understanding of nature.

Since 1973, our knowledge of elementary particles and fundamental forces has been expressed in what we call the Standard Model of Elementary Particle Physics. This reduces all phenomena, save gravity, to twelve fundamental particles interacting via three forces. This Standard Model has been confirmed in all experiments to date. This includes new measurements announced this month by two teams of experimentalists operating the Atlas and CMS detectors at the Large Hadron Collider (LHC), working at nearly twice the energy as previous experiments.

In 2012, the news from the LHC was the discovery of the Higgs, which was the last particle predicted by the Standard Model remaining to be discovered. But the Standard Model cannot be the whole story, in part because the model involves twenty-nine free parameters. We have no explanation for the values of these parameters and hence seek a deeper theory “beyond the Standard Model” that would explain them. Moreover, many of these values seem extremely unnatural: they are very tiny numbers with large ratios among them, (the hierarchy problem) and they seem to be tuned to special values needed for a universe with many stable nuclei allowing complex life to exist (the fine tuning problem). In addition, there is no reason for the choices of the fundamental particles and for the symmetries that govern the forces between them. Another reason for expecting new particles beyond the Standard Model is that we have excellent evidence from astronomy for dark matter, which gravitates but doesn’t give off light. All these pieces of evidence point to new phenomena that could have been discovered at the LHC.

Several beautiful hypotheses have been offered on which to base a deeper unification going beyond the Standard Model. I will just give the names here: supersymmetry, technicolor, large extra dimensions, compositeness. These each imply that new particles should have been discovered at the LHC. Some also point to more exotic phenomena such as quantum black holes. To date, the experimental evidence sets impressive limits against these possibilities.

To be sure, there is one weak, but very exciting, indication from new results that might be interpreted as a signal of a new particle beyond the Standard Model. This is a small excess of collisions which produce pairs of photons that, remarkably, are seen by both of the experiments operating at the LHC. But the statistical significance is not high when the statistics is done taking into account that one is bound to get some signal by random chance in one of the many channels looked at. So it could be a random fluctuation that will go away when more data is taken.

Even if this hint grows into the discovery of a new particle, which would be extremely exciting news, it is too soon to say whether it will lead to a deeper unification, rather than just add complication to the already complicated Standard Model. Luckily, more data can be expected soon.

It is the same in quantum gravity, which is the unification of quantum theory with Einstein’s theory of gravity. Many proposals for quantum gravity suggest that at certain extremely high energy scales we must see new physics. This would indicate that at correspondingly tiny scales space becomes discrete, or new features of quantum geometry kick in. One consequence would be that the speed of light is no longer universal—as it is in relativity theory—but would gain a dependence on energy and polarization visible at certain scales.

In the last decade this prediction has been tested by sensitive measurements of gamma rays that have traveled for billions of years from extremely energetic events called gamma ray bursts. If the speed of light depends even very slightly on energy, we would see higher energy photons arriving systematically earlier or later than lower energy photons. The enormous travel time would amplify the effect. This has been looked for by the Fermi satellite and other detectors of gamma rays and cosmic rays. No deviations from relativity theory are seen. Thus, our best hope of discovering quantum gravity physics has been frustrated.

A similar story seems to characterize cosmology. Something remarkable happened in the very early universe to produce a world vast in scale but, at the same time, extremely smooth and homogeneous. One explanation for this is inflation—a sudden enormous expansion at very early times, but there are competitors. Each of these theories requires delicate fine tuning of parameters and initial conditions. Once this tuning is done, each predicts a distribution of noisy fluctuations around the smooth universe. These show up as a seemingly random distribution of very slightly denser and less dense regions. Over hundreds of millions of years of expansion, these amplify and give rise to the galaxies. These fluctuations make bumps that are visible in the cosmic microwave radiation. So far, their distribution is as random, featureless, and boring as possible, and the simplest theories—whether inflation or its alternatives—suffice to explain them.

In each of these domains we have sought clues from experiments into how nature goes beyond, and solves the puzzles latent in, our incomplete theories of the universe, but we have so far come up with nearly nothing.

It is beginning to seem as if nature is just unnaturally fine tuned. In my opinion we should now be seeking explanations for why this might be. Perhaps the laws of nature are not static, but have evolved through some dynamical mechanism to have the unlikely forms they are observed to have.

The discovery that dinosaurs of the Velociraptor type had ample feathers and looked more like ostriches than the slick beasts we have become accustomed to from the Jurassic Park movies was my favorite scientific finding of 2015.

The specific discovery was the genus Zhenyuanlong, but it tells a larger story. Feathered dinosaurs have been coming out of the ground in China almost faster than anyone can name them since the 1990s. However, it has taken a major adjustment to allow that the feathers on these dinosaurs mean that the equivalent dinosaurs in other parts of the world had feathers as well. The conditions in China simply happen to have been uniquely well suited to preserve the feathers’ impressions. That palaeontologists now know that even the Velociraptor type had feathers is a kind of unofficial turning point. No longer can we think of feathered dinosaurs as a queer development of creatures in East Asia. We can be sure that classic dinosaurs of that body type traditionally illustrated with scaly lizard-type skins—dino fans of my vintage will recall Coelophysis, Ornitholestes, etc.—had feathers. For example, other evidence of this kind came in 2015 too, including the discovery of strikingly extensive evidence of feathers on dinosaurs long known as the "ostrich-like" sort—Ornithomimus. Little we knew how close that resemblance was.

Who really cares whether Velociraptor had feathers, one might ask. But one of the key joys of science is discovering the unexpected. One becomes a dino fan as a kid from the baseball-card collecting impulse, savoring one’s mental list or cupboard of names and types. However, since the seventies it has become clear first that birds are the dinosaurs that survived, but then, even more dramatically, that a great many dinosaurs had feathers just like birds. This shouldn’t be surprising, but it is. Dinosaurs have gone from hobby to mental workout.

Second, the feathered Velociraptor coaxes us to tease apart the viscerally attractive from the empirically sound. The truth is that the sleek versions of bipedal dinosaurs look "cool"—streamlined, shiny, reptilian in a good way. Nothing has made this clearer than the Velociraptors brought to life in the Jurassic Park films. For these creatures to instead look more like ostriches, sloshing their feathers around and looking vaguely uncomfortable, doesn’t quite square with how we are used to seeing dinosaurs. Yet that’s the way it was. The sleek-looking Velociraptor, or little "Compies" of the Jurassic Park films (Compsognathus), or any number of other dinosaurs of that general build, are now "old school" in the same way as Brontosauruses lolling around in swamps (they didn’t) and Tyrannosauruses dragging their tails on the ground.

Finally, Velociraptor as ostrich neatly reinforces for us the fact that evolution works in small steps, each of which is functional and advantageous at the time, but for reasons that can seem quite disconnected from the purpose of the current manifestation of the trait. In life in general this is a valuable lesson.

In a language that marks articles, adjectives, and nouns with an arbitrary gender like Spanish (“the white house” is la casa blanca), the gender marking helps keep it clear how the words relate to one another. But such marking originates from a division of nouns into understandable classifications as "masculine" and "feminine" (or animal, long, flat, etc., depending on the language) whose literal meanings fade over time and just leave faceless markers.

In the same way, an ostrich-sized dinosaur with feathers rather clearly wasn’t capable of soaring like an albatross, which is one of many pieces of evidence that feathers emerged as insulation and/or sexual display, and only later evolved to allow flight.

Not that dinosaurs existed to compliment the aesthetics, tastes, and nostalgic impulses of observers millions of years after their demise, but feathered dinosaurs are tough to adjust to. Yet the adjustment is worth it, as it makes dinosaurs more genuinely educational in many ways.

Evidence has recently piled up that there is a gigantic black hole, Sagittarius A*, with a mass 4 million times that of our Sun, at the center of our galaxy. Similar black holes appear to exist at the center of most galaxies. Some are even much bigger—with masses billions times that of our Sun. Can you imagine a black hole a billion times the size of our Sun?

The existence of these giants, once again, changes our picture of the universe. Clearly these monsters must have played a major role in the history of the cosmos, but we do not know how. Astronomers are building a "telescope" as large as the Earth, connecting in line many existing radio telescopes to actually see Sagittarius A* directly. It is called the "event horizon" telescope.

But these immense holes are also the boundary of our current knowledge: we see matter falling into them—we have no idea what ultimately happens to it. Space and time appear to come to an end inside. Or, better said, to morph into something we do not yet know. The universe is still full of mystery.

Over the past few years, a raft of classic challenges in artificial intelligence which had stood unsolved for decades were conquered, almost without warning, through an approach long disparaged by AI purists for its "statistical" flavor: it's essentially about learning probability distributions from large volumes of data, rather than examining humans' problem-solving techniques and attempting to encode them in executable form. The formidable tasks it has solved range from object classification and speech recognition, to generating descriptive captions for photos and synthesizing images in the style of famous artists—even guiding robots to perform tasks for which they were never programmed!

This newly dominant approach, originally known as "neural networks," is now branded "deep learning," to emphasize a qualitative advance over the neural nets of the past. Its recent success is often attributed to the availability of larger datasets and more powerful computing systems, or to large tech companies' sudden interest in the field. These increasing resources have indeed been critical ingredients in the rapid advancement of the state of the art, but big companies have always thrown resources at a wide variety of machine learning methods, and it's deep learning in particular that has seen such unbelievable advances; many other methods have also improved, but to a far lesser extent.

So what is the magic that separates deep learning from the rest, and can crack problems for which no group of humans has ever been able to program a solution? The first ingredient, from the early days of neural nets, is a timeless algorithm, rediscovered again and again, known in this field as "back-propagation." It's really just the chain rule—a simple calculus trick—applied in a very elegant way. It's a deep integration of continuous and discrete math, enabling complex families of potential solutions to be autonomously improved with vector calculus.

The key is to organize the template of potential solutions as a directed graph (e.g., from a photo to a generated caption, with many nodes in between). Traversing this graph in reverse enables the algorithm to automatically compute a "gradient vector," which directs the search for increasingly better solutions. You have to squint at most modern deep learning techniques to see any structural similarity to traditional neural networks, but behind the scenes this back-propagation algorithm is crucial to both old and new architectures.

But the original neural networks that used back-propagation fall far short of newer deep learning techniques, even using today's hardware and datasets. The other key piece of magic in every modern architecture is another deceptively simple idea: components of a network can be used in more than one place at the same time. As the network is optimized, every copy of each component is forced to stay identical (this idea is called "weight-tying"). This enforces an additional requirement on weight-tied components: they must learn to be useful in many places all at once, and not specialize to a particular location. Weight-tying causes the network to learn a more generally useful function, since a word might appear at any location in a block of text, or a physical object might appear at any place in an image.

Putting a generally-useful component in many places of a network is analogous to writing a function in a program and calling it in multiple spots—an essential concept in a very different area of computer science, functional programming. This is actually more than just an analogy: weight-tied components are actually the same concept of reusable function as in programming. And it goes even deeper! Many of the most successful architectures of the past couple years reuse components in exactly the same patterns of composition generated by common "higher-order functions" in functional programming. This suggests that other well-known operators from functional programming might be a good source of ideas for deep learning architectures.

The most natural playground for exploring functional structures trained as deep learning networks would be a new language that can run back-propagation directly on functional programs. As it turns out, hidden in the details of implementation, functional programs are actually compiled into a computational graph similar to what back-propagation requires. The individual components of the graph need to be differentiable too, but Grefenstette et al. recently published differentiable constructions of a few simple data structures (stack, queue, and deque), which suggests that further differentiable implementations are probably just a matter of clever math. Further work in this area may open up a new programming paradigm—differentiable programming. Writing a program in such a language would be like sketching a functional structure with the details left to the optimizer; the language would use back-propagation to automatically learn the details according to an objective for the whole program—just like optimizing weights in deep learning, but with functional programming as a more expressive generalization of weight-tying.

Deep learning may look like another passing fad, in the vein of "expert systems" or "big data." But it's based on two timeless ideas (back-propagation and weight-tying), and while differentiable programming is a very new concept, it's a natural extension of these ideas that may prove timeless itself. Even as specific implementations, architectures, and technical phrases go in and out of fashion, these core concepts will continue to be essential to the success of AI.

What has amazed and excited me the most in recent scientific news is that the concept of trust can be measured validly and reliably and that it organizes a vast amount of information about what makes families and human societies function well, or fail.

As a relationship researcher and couples-family therapist, I have known for decades that trust is the number one issue that concerns couples today. Consistent with this truth is the finding that the major trait people search for in trying to find a mate is trustworthiness. Robert Putnam’s groundbreaking book Bowling Alone began documenting this amazing field of scientific research. It is based on a very simple question. Sociologists have used a yes/no survey question: "In general, would you say that you trust people?" It turns out that regions of the USA, and countries throughout the world vary widely in the percentage of people who answer that question by saying "yes."

Here’s the amazing scientific news. In the USA the percentage of people who trust others in a region correlates highly with a vast array of positive social indices such as greater economic growth, the greater longevity of citizens, their better physical health, lower crime rates, greater voting participation, greater community involvement, more philanthropy, and higher child school achievement scores, to mention just a few variables that index the health of a community. As we move from the North to the South in the United States, the proportion of people who trust others drops continuously. A great archival index of trust turns out to be the discrepancy in income between the richest and the poorest people in a region.

High income discrepancy implies low trust. That discrepancy has been growing in the USA since the 1950s, as has the decline in community participation. For example, data show that in the 1950s CEOs earned about 25 times more than the average worker, but that ratio grew steadily so that in 2010 that ratio was about 350 times more. So we are in a crisis in the USA, and it’s no surprise that this difference between the rich and the poor has become a major issue in the 2016 election. One amazing fact in these results is the following: how well our country cares about its poorest citizens is actually a reliable index of the social and economic health of the entire country. Therefore, an empirical finding is that empathy for the poor is smart politics.

These results also hold internationally, where the trust percentage is also related to less political corruption. Only 2 percent of the people in Brazil trust one another, whereas 65 percent trust others in Norway. While many other factors are important internationally, we can note that today Brazil is experiencing vast amounts of chaos, while Norway is thriving.

These spectacular data are, unfortunately, correlational. Of course, it is hard to do real experiments on societal levels. However, these findings on trust have now spawned new growing academic fields of behavioral economics, and neuro-economics. These fields are generating exciting new experiments.

This breakthrough trust work, combined with the mathematics of Game Theory, has led to the creation of a valid "trust metric" in interactions between two people. A new understanding of the processes of how two people build or erode trust in a love relationship has spawned a new therapy that is currently being tested.

What this means to me is that we are coming very close to an understanding of human cooperation in family relationships that generalize to society as a whole. I am hopeful that these breakthroughs may eventually lead us to form a science of human peace and harmony.

If a central problem in the contemporary world could be defined, it might be called adapting ourselves to algorithmic reality. The world is marked by an increasing presence of technology and the key question is whether we will have an empowering or enslavening relation with it. To have an enabling relation, we may need to mature and grow in new ways. The fear is that just as human-based institutions can oppress, so too might technology-orchestrated realities, and in fact this case might be worse. Blockchain technology is the newest and most emphatic example of algorithmic reality, news that makes us consider our relation with technology more seriously.

Blockchain technology (the secure distributed ledger software that underlies cryptocurrencies like Bitcoin) connotes the Internet II: the transfer of value, as a clear successor position to the Internet I: the transfer of information. This means that all human interaction regarding the transfer of value, including money, property, assets, obligations, and contracts could be instantiated in blockchains for quicker, easier, less costly, less risky, and more auditable execution. Blockchains could be a tracking register and inventory of all the world’s cash and assets. Orchestrating and moving assets with blockchains concerns both immediate and future transfer, whereby entire classes of industries, like the mortgage industry, might be outsourced to blockchain-based smart contracts, in an even more profound move to the automation economy. Smart contracts are radical as an implementation of self-operating artificial intelligence, and also through their parameter of blocktime, rendering time too to be assignable rather than fixed.

Blockchains thus qualify as the most important kind of news, the news of the new—something that changes our thinking; causes us to pause; where in a moment we instantly sense that now things might be forever different as a result.

Singularity-class science and technology breakthroughs are news of the new. It is revolutionary what advances like deep learning, self-assembling nanotechnology, 3D printed synthetic biological life, the genome and the connectome, immersive virtual reality, and self-driving cars might do for us. However impressive though, these are context-specific technologies. So when a revolution comes along that is as general and pervasive as to possibly reorchestrate all of the patterns of life, that is noticeable news of the new, and that is blockchains.

In existing as a new general class of thing, blockchains reconfigure the definition of what is it is be new. Perhaps we did not even know what the new, or news, was before, or how new something could be. We see the elegance of Occam’s razor in redefining what it is to be new. The medium and the message are simultaneously the message; the message is stronger because it has reconfigured its own form. The blockchain redefines what it is to be new because the medium and message create an entirely new reality and possibility space for how to do things. Where previously there were only hierarchical models for organizing large-scale human activity, blockchains open up the possibility of decentralized models, and hybrid models; and not just improved current methods, but an expansive invitation to new projects. The blockchain news of the new is that we are in a whole new possibility space of diversity and decentralization, no longer confined to hierarchy as a solitary mode of coordination.

Blockchains as a class of the new startle us with the discomfort and fear of the unknown because it is not clear if blockchain technology is good or bad news. Even more fearful is perhaps the implication that in fact it is us who will determine whether the news is good or bad. The underlying technology is as any other technology, itself neutral or at least pliable in dual-use for both helpful and limiting purposes. Blockchains invite the possibility of action, and more deeply, the responsibility of action. We are developing the sensibility of the algorithmically-aware cryptocitizen, where the expansion of the apparatus of our thought and maturity is proportional to the force of the new experience to be mastered.

Cryptocitizen sensibility and the blockchain news of the new invite the possibility of our greater exercise of freedom and autonomy, and rethinking authority. In a Crypto Copernican turn, we can shift the assumed locus of authority from being outside ourselves in external parties to instead residing within our own self. This is the Enlightenment that Kant was after, an advance not just in knowledge but also in authority-taking. A Cambrian explosion of experimentation in new models of economics and governance could ensue. Blockchains, being simultaneously global and local, could coordinate the effort, equitably joining the diversity of every permutation of micropolis with the cohesion of the macropolis, in connected human activity on digital smartnetworks.

When we have an encounter with the new, that is indeed news. This is the news that we crave and seek above all else, an encounter with the sublime that reconfigures our reality. The galvanizing essence of the blockchain news of the new is the possibility of our further expansion into our own potentiality. The Crypto Enlightenment is the kind of authority-taking change we can make within ourselves as a maturation towards the realization of an empowering relationship with technology.

As an aphorism, that isn’t news at all. 2300-plus years ago, Aristotle wrote in his Politics: “It is evident that the state is a creation of nature, and that man is by nature a political animal.” He thought we were even more political than bees.

But that apothegm became science after Darwin traced H sapiens’ descent from apes, and generations of Darwin’s scientific descendants—especially the last generation—followed up with field studies of hundreds of other social, or “political,” animals.

They showed that whenever animals get together, they play by the same rules. When groups form in vaguely delineated habitats, where the costs of emigrating are low, they tend to be quasi- (or “sort of”) social. Most animals help raise the young, and most animals reproduce. But when groups form in sharply delineated habitats, where the costs of emigrating are high—say, in the tree hollows where thousands of honeybees raise larvae produced by their queen—they’re often eu- (or “truly”) social. Some animals are breeders. Other animals work.

As long as they kept moving, most H sapiens probably had their own children. For roughly 100,000 years, they ran around the subSahara; then around 100,000 years ago, they ran out of Africa. It wasn’t until after around 10,000 years ago that they settled down in the Fertile Crescent, and their societies started to look like bees’.

Their hagiographers had medieval missionaries soak up chastity like honey; and some of the abbots who worked under Charlemagne were referred to as apēs. St Ambrose, who venerated virginity, was discovered in his Trier cradle with a swarm of bees in his mouth, and ended up as a honey-tongued bishop; St Bernard, who founded a monastery in Burgundy, and venerated the Virgin Mary, was remembered as Doctor Mellifluous. But most helpers in the middle ages held onto their genitals.

Workers in the first civilizations did not. There were apiaries in ancient Israel, the land of milk and honey, where David made his son Solomon a king in front of an assembly of eunuchs; and there were apiaries in Egypt, where pharaohs put bees on their cartouches, and may have collected civil servants who lacked generative powers. There were more beekeepers in Mantua, where Publius Vergilius Maro grew up. For the benefit of the first Roman emperor, Augustus, he remembered how honeybees fought and foraged for their monarch, made honey and nursed their larvae. In the end, unmarried soldiers, unmarried slaves, and the unmanned attendants of the sacred bedchamber effectively ran that empire. And the emperor bred.

The take-home message from all of which is simple. It’s good to be mobile. Societies, human or otherwise, are politically and reproductively more equal when emigration is an option. They’re less equal when that option is closed.

There certainly were plenty of remarkable discoveries this past year, my favorite demonstrating that running promotes the generation of new neurons in the aging brain. That prompted me to get back on the treadmill after a hiatus of more than a year. But the big scientific news story for me was not any single event. Rather I have been struck by the emergence over the past several years of two seemingly disparate yet related trends in the scientific world. Neither of these has made front-page news, although both are well known to those of us in the science business.

The first of these is the apparent increase in the reported incidence of research findings that cannot be replicated. The causes for this are myriad. In some cases, this is simply because some vital piece of information, required to repeat a given experiment, has been inadvertently (or at times intentionally) omitted. More often, it is the result of sloppy work, such as poor experimental design, inappropriate statistical analysis, or lack of appropriate controls. But there is also evidence that scientific fraud is on the increase. A number of sensational exposes have come to light this past year, and the incidence of retracted, withdrawn and corrected scientific papers has increased steadily over the past decade. Indeed, some pharmaceutical companies have made the decision to no longer rely on the results of published studies because they feel that in many cases these are not trustworthy. Some have argued that the increase in the incidence of scientific misconduct reflects the use of new technologies for uncovering scientific misconduct, such as programs that check for plagiarism. Technological advancements have certainly played an important role, but in my opinion are unlikely to explain entirely the failure to replicate deluge.

A more compelling explanation is the fiercely competitive nature of science that has accelerated tremendously in recent years. Grants are much harder to get funded, so that even applications ranked by peer review as “very good” are no longer above the pay line. This means that faculty at research universities must spend the bulk of their time writing grant proposals with the probability of funding often being less than ten percent. At the same time, the most highly ranked scientific journals have increased their rejection rates considerably with acceptance rates in the neighborhood of five percent being not uncommon. Moreover, to get a final acceptance for publication in the top journals, the editors (based on the recommendations of anonymous referees) often request additional experiments requiring considerable more time, expense and effort without any guarantee that the final product will be accepted for publication. For practitioners of science the level of stress has never been greater. Is it any wonder that some will succumb to a short-cut method to success by “fudging” their results to get a competitive edge.

I am not suggesting that simply increasing the amount of available funding for research would abate this problem. The solution will require a multi-faceted approach. One step that should be seriously considered is a substantial increase in the penalty for scientific fraud, with jail time for those that have wasted precious research dollars. Today, those that are caught often get away relatively unscathed, in some instances receiving a substantial buy-out package to leave their place of employment due to the nature of the university tenure system. This trend must be reversed to ensure the viability of the scientific enterprise.

In a recent U.S. Supreme Court hearing regarding affirmative action in higher education, a Justice posed the question, “What unique perspective does a minority student bring to a physics class?” If physics were the work of robots, the answer would be none. But physics, as all of science, is approached with the biases and perspectives that make us human. Both disciplined and spontaneous, science is an intuitive as well as a systematic undertaking.

It is the people who comprise physics graduate schools, institutes, and faculty—their interests, their backgrounds, their agendas—who drive the direction of physics research and scholarship. And it does not take much imagination to picture what different directions this research might take, were the pool of scientists not heterogeneous.

Science has become increasingly collaborative in a way that makes diversity a paramount necessity. Until recently it was the work of single individuals, primarily white males from northern Europe. It was rare to find a published paper with two authors and one with more than three was essentially unheard of. A change began at the time of World War II and it has grown since then.

Large science collaborations encompassing diversity of gender, race and ethnicity have become a new norm. ATLAS, a group that has been a major contributor to CERN’s discovery of the Higgs boson consists of 3,000 physicists from 175 institutions in 38 countries working harmoniously together. Even though a single large instrument such as a particle accelerator or a large telescope is not required in biology, we see parts of the field moving in the same direction with the Human Genome or Human Microbiome project. A different kind of complexity, the assembling of disparate parts of the pattern is needed there.

The news is that science’s success in such endeavors is creating a recognized model for international collaboration, climate change being the most conspicuous example.

The diversity in approach engendered by diversity in background has been a powerful combination in science. It is no secret that in a fairly recent past if physics graduate school admission had been based on achievement tests, the entering class would have been composed almost entirely of students from mainland China. Most of these schools believed that such a homogeneous grouping would not have benefited either the students or the field. It would have reinforced conformity rather than encouraging the necessary originality and entrepreneurship. It would have reinforced conformity rather than challenge assumptions.

Science’s future, both in the classroom and in research, is tied to an increased ability to achieve diversity in gender, race, ethnicity, and class. If those communities of learning are not established in a classroom setting, science will suffer.

This year I had the wonderful and shocking awareness that I’m not only connected to microbes but, in a way, I’m so dependent on them, I sort of am them.

Darwin gave me the understanding that I’m related to the rest of the beasts of the earth, but work on the microbiome released in 2015, impressed me with how much a part of me microbes really are and how much I look to them for my very existence.

It started with the spooky information a short while back, that there are 10 times as many of them in me as there are me in me; at least if you compare the number of their cells in me to the number of mine in me.

From what I read, they’re so specialized that the microbes in the crook of my arm are more like the ones in the crook of your arm than they are like the microbes in my own hand.

Then came the discovery that before long I’ll be able to get a fecal transplant, or maybe simply take a poo pill, to relieve all kinds of disturbances in my body—possibly even obesity, should it ever win the war against my self control.

And there was the equally strange news that I give off a cloud of microbes wherever I go—and if they settle on a surface, someone could take a reading and record a kind of fingerprint of my personal microbiome, after I’d left the scene.

They’re ubiquitous little guys. There are, I believe, more of them pound for pound than any other living thing on earth, and we can’t even see them.

And they’re powerful. One kind of microbe expands when wet, and a pound or two of them could lift a car a couple of feet off the ground. You could change your tire with them.

We’ve planted a flag on a new New World. The last frontier has just changed again, from outer space, to the brain, to an invisible world, without which there would be no world as we know it.

Aristotle discussed magnetism with Thales of Miletus. Oriental medicine refers to the meridian circles and has been treating by using the magnetic field before the invention of the acupuncture needle in the Iron Age. As the Italian philosopher Benedetto Croce has been quoted “All history is the contemporary history.”—the magnet’s cryptographic character, relevant from mimicking, daily objects, the computer network, medical devices, and space expeditions through electromagnetic fields that linked multiple cultural devices in our saturated era—these scientific developments, as a decorous bridge between the 21st and the 22nd century, will still innovate the world by giving a resolution. Far from the extreme division of its character, the magnet espoused technology and contemporary in which “laissez faire” free trade would make peace in our world.

Imagine that a friend you trust tells you a rumor. It’s an unlikely story and they aren’t completely sure of themselves. But a few minutes later another friend cautiously tells you the same thing. The combination of two similar stories from two reputable witnesses makes you want to explore further. This is what happened on December 15, 2015 when the ATLAS and CMS experiments at CERN shared an initial analysis of their highest-energy run, with the same hint of something interesting in the data. It’s too early to claim a new particle, but the situation in particle physics makes this the biggest news in our recent history.

The Large Hadron Collider’s (LHC) energy-breaking run was launched with a vengeance in 2010, following an incident that damaged the machine in 2008 and a cautious year in 2009. The 2011-2012 dataset delivered the discovery of the Higgs boson. As data streamed in, particle physicists around the world clustered in conversations around espresso machines and would land on this sobering scenario: What if we find the Higgs boson and nothing else? In other words, what if we neatly categorize the particles predicted by the Standard Model, incorporating the Higgs mechanism to provide mass, but have no progress toward understanding the nature of dark matter, dark energy, quantum gravity, or any clues to explain the 96 percent of the matter/energy content universe that is not incorporated in the theory?

If physics is a valid framework for understanding the universe, there is something else out there for us to discover. The prediction of the Higgs, the ultimately successful decades-long campaign to discover it, and the ongoing partnership between experiment and theory to characterize it, gives us confidence in the methods we are using. But the missing pieces could be beyond our imaginations, the current state of our technology, or both.

The terrifying possibility floating through these “Higgs and nothing else” conversations is that we might reach the end of exploration at the energy frontier. Without better clues of our undiscovered physics, we might not have sufficient motivation to build a higher energy machine. Even if we convince ourselves, could we convince the world and marshal the necessary resources to break the energy frontier again and continue to probe nature under the extreme conditions that teach us about nature’s building blocks?

In 2015, the LHC broke another energy barrier for hadron colliders with a jump from the 8 TeV center of mass collisions that delivered the Higgs discovery to 13 TeV center of mass. The ATLAS and CMS collaborations worked 24/7 to analyze the data in time for a presentation at an end of year event on December 15. Both experiments cautiously reported the hint of a new particle with the same signature in the same place. Two photons caught in high-energy collisions can be arranged together to form a mass, as if they originated from the same single particle. Using this technique, both experiments saw a slight clustering of masses—more than what was expected from the Standard Model alone—near 750 GeV/c2. The experiments are cautious for a good reason. These hints, in the same place for ATLAS and CMS, could disappear with the gathering of data in 2016. If the situation were less dire, this would not be big news. But for me it represents all of the promise of our current energy-frontier physics program.

Over the next few years we will gather ten times the data at 13 TeV than we currently have in hand, and we have theoretical motivation to expect something right around the corner. If the 750 GeV hint disappears with increased statistics, we will keep searching for the next hint that could break open our understanding of nature. We move forward with the next step potentially within our reach, determined to find it, if it’s there.

Some 2000 years ago, probably 23 BCE, the Roman poet Horace (Quintus Horatius Flaccus) published some poems, and they stayed forever as he predicted himself: “have built a monument which will last longer than ore” (Exegi monumentum aere perennius). Although these words about his odes were not exactly an expression of modesty, he was right. The most famous ode (number 11 of the first book) is also one of the shortest with only eight lines. Everyone knows the words enjoy the day (carpe diem); these two words imply more than just having fun, but to actively grasp the opportunities of the day and to “seize the present” as someone has translated into English.

This ode of Horace starts with the energetic advice not to ask questions that cannot be answered. This is an eternal reminder not only for scientists, i.e., very old news that have to be repeated regularly. Science is to discover right and good questions, indeed often unasked questions, before trying to give an answer. But what are criteria for right questions? How do we know that a question can be answered and does not belong to the realm of irrationality? How can a mathematician trust his mind that a proof will be possible, and then he spends years to find it? Apparently, the power of implicit knowledge or intuition is much stronger as we are usually inclined to believe. It is attributed to Albert Einstein to have said: “The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift.

In poetry we often find representations of such implicit knowledge and intuition with high scientific value opening new windows of potential discoveries. It just has to be harvested. If read (better even spoken) with an open mind, poetry can serve as a direct bridge, an effortless link between the different cultures. Thus, poetry does not belong to the humanities only, (if at all); poems in all languages express anthropological universals and cultural specifics in a unique way, and they provide insights into the human nature, the mode of thinking and experiencing, often shadowed by a castrated scientific language.

After his warning with respect to questions that cannot be answered Horace suggests that one should not fall into the trap to gamble; one simply should accept reality (ut melius quidquid erit pati), and he gives the frustrating but good advice to bring our great hopes into a smaller space (spatio brevi spem longam reseces). This is hard to take as scientists always want to go beyond not accepting the limits of our mental power. But it is good to be always reminded that our evolutionary heritage has dictated limits of reasoning and insights which have to be accepted and which should be the basis of modesty.

Such limits are also pointed out in other cultures; for instance more than 2000 years ago Laozi in the Daodejing says: “To know about not knowing is the highest” (in pinyin with indication of the tones: zhi-1 bu-4 zhi-1, shang-4). To accept such an attitude is not easy, and it may be impossible to suppress the search for causality as expressed by the French poet Paul Verlaine: It is the greatest pain not to know why (C'est bien la pire peine de ne savoir pourquoi).

Apparently, poets (of course not all of them) have some knowledge about our mental machinery which can guide scientific endeavors. But there is also a problem which is language itself: Can poetry be translated? Can even scientific language be translated veridically? Of course not. Take the English translation seize the present of carpe diem. Does the English present cover equivalent connotations in Chinese, German or any other language? The English present evokes different associations compared to the German “Gegenwart” or the Chinese “xianzai”. Present is associated with sensory representations, whereas “Gegenwart” has a more active flavor; the component “warten” refers either to take care of something" or "to wait for something", and it is thus also past and future oriented. The Chinese “xianzai” is associated with the experience of existence in which something is accessible by its perceptual identity; it implies a spatial reference indicating the here as the locus of experience, and it is also action oriented. Although the different semantic connections are usually not thought of explicitly, they still may create a bias within an implicit frame of reference.

What follows? It is necessary to realize that the language one uses, also in scientific discourse, is not neutral with respect to what one wants to express. But this is not a limitation; if one knows several languages, and a scientist knows several languages anyway, it is a rich source of creativity. Some sentences, however, do not suffer from translations; they are easily understood and they last forever. When Horace says that while we talk the envious time is running away (dum loquimur fugerit invida aetas), one is reminded of scientific (and political) discussions full of words with not too much content; not exactly new news.

This year, members of the Mashco-Piro tribe—previously viewed as one of the few remaining "uncontacted" peoples—emerged to make increasing contact with the outside world. But this was less than a heart-warming story: as is so often the case with such contact, members of the tribe were vulnerable to unfamiliar diseases such as influenza. These active efforts to become "contacted" create a problem for countries like Brazil which have initiated far-sighted "no contact" policies to allow such tribes to choose seclusion. As José Carlos Meirelles, an Indian protection agent in Brazil, put it: "If they are seeking contact, we must welcome them in the best manner possible. We must take care of their health, block out the boundaries of their territory, give them some time to adjust to the madness of our world."

This year, we also learned more about the role of Catharine Conley, "planetary protection officer" at NASA, who has the job not of finding life on Mars but of protecting Mars from life on Earth. Scientists agree that life exists on Mars, if only in the form of microbes from Earth that took an accidental interplanetary ride. Despite the best efforts of NASA—which include sterilizing and sometimes even baking spacecraft—some life slips through. Much like the "no contact" policies for uncontacted peoples, NASA has protocols that keep rovers on Mars far from "special regions" where bacteria from Earth might thrive.

But, what happens when life on Mars chooses not to wait? All of recorded history shows that life tends to find life—or more likely in this case, lichens tend to find lichens. Surely we should apply the lessons learned from centuries of genocide (accidental and intentional) of indigenous peoples on this planet to nascent forms of life on Mars. "If they are seeking contact, we must welcome them in the best manner possible. We must take care of their health, block out the boundaries of their territory, give them some time to adjust to the madness of our world."

The all-consuming news story in biology this year—this decade really—is the discovery of the CRISPR/Cas9 system and its practical application for gene editing. At least three other essays in this book mention or refer to this new technology; there have been numerous articles in the popular press. Most of that attention has been directed at the tremendous and potentially dangerous power of this new technology: it allows "editing" the DNA of genomes, including those of humans, in a way that would be permanent—that is heritable through generations.

All this attention on the possible uses and misuses of CRISPR/Cas9 has obscured the real news—which is, in a way, old news. CRISPR/Cas9 is the fruit of years of fundamental research conducted by a few dedicated researchers who were interested in the arcane field of bacterial immunity. Not immunity to bacteria as you might at first think, but how bacteria protect themselves against attack by viruses. Weird as it may seem, there are viruses that specialize in attacking bacteria. Just as most viruses we know about are specific to one species or another—you can’t catch a cold from your dog—there are viruses that only infect bacteria, in fact only certain types of bacteria. These have a special name—phage. And they have a long history in the development of molecular biology and genetics. Indeed molecular biology began with the study of phage and its ability to insert its genome into the genome of bacteria—even before Watson and Crick’s famous articulation of DNA as the molecule of heredity.

For the past forty years restriction enzymes, another family of bacterial proteins, have been the mainstay of the biotech industry, and they too were discovered first as an early example of bacterial mechanisms to protect themselves against invading viruses. And they were also discovered in university research laboratories devoted to fundamental basic research. CRISPR/Cas9 however is a more sophisticated mechanism, approaching that of the immune system of higher animals: it is adaptive, in the sense that a bacterium and the other bacteria it generates by dividing can "learn" and destroy the DNA of the genome of a particular type of phage after it has been attacked by that phage once. The researchers who discovered CRISPR/-Cas9 and recognized its potential value as a gene editing tool in living things other than bacteria were not searching for some new technology, they were after a deeper understanding of a fundamental question in prokaryotic (microbial) biology and evolution—the back and forth competition between bacteria and the viruses that invade them. Could that be any more arcane sounding?

It is also important to recognize that this was not a serendipitous discovery, a happy accident along the way. This is often the case made for supporting fundamental research—that you never know where it might lead, serendipity intervenes so often. But this is a false conception and CRISPR/Cas9 is a perfect example of why. This was no simple accident resulting from good luck or happenstance. It was the fruit of hard and sustained labor, of whole careers devoted to understanding the fundamental principles of life. Work in this area goes back thirty years, and the particular groups that discovered CRISPR/Cas9 were looking for precisely such an adaptive, immune-like response in bacteria. Understanding the value of restriction enzymes, as these researchers would have, was a sensible leap to appreciating the value of a potentially even more sophisticated DNA-based protective system. This is how research works—neither by accident nor by purpose—it is the result of hard work at every level of inquiry. Indeed advances are often unpredictable, but that doesn’t make them merely lucky. Certainly not like winning some type of lottery.

We continue to have this misguided debate about fundamental versus applied research as if they were two spigots that can be operated independently. They are one pipeline, and our job is to keep it flowing. This is old news, but we should never tire of saying it. And in this case it created the biggest news in biology in a decade—CRISPR/Cas-9.

News stories are by their nature ephemeral. Whipped up by the media (whether mass or social), they soon dissipate, like ripples on the surface of the sea. More significant and durable are the great tides of social change and technological progress on which they ride. It is these that will continue to matter in decades and generations to come. Fortunately, like real tides, they are more predictable too.

One such inexorable trend is our changing relationship with the natural world—most vividly represented by the ongoing debate about whether humanity's impact has been so profound as to justify the christening of a new geological epoch: the Anthropocene. Whether or not a consensus emerges in the next few years, it will do so eventually, for our effect on the planet will only grow. This is in part because our technological capabilities continue to expand, but an even more important driver is our evolving collective psyche.

Since Darwin showed us that we are products of the natural world rather than its divinely appointed overlords, we have become reticent to fully impose our will, fearful of our own omnipotence and concerned that we will end up doing more harm than good. But however determinedly we draw such red lines we might as well be trying, Canute-like, to hold back the sea. For whatever is beyond the pale today will eventually come to seem so natural that it will barely register as news—even if it takes the death of the old guard to usher in a new way of thinking. To future generations, genetic engineering of plants and animals (and humans) will seem as natural as selective breeding is today, and planetary-scale geoengineering will become as necessary and pervasive as the construction of dams and bridges.

As for our place in nature, so too for our relationship with technology. Recent progress in artificial intelligence and bionics, in particular, have led to a great deal of soul-searching about who—or what—is in charge, and even what it means to be human. The industrial revolution saw machines replace human physical labor, but now that they are replacing mental labor too, what will be left for people to do? Even those who don't fear for their jobs might be angry when they discover that their new boss is an algorithm.

Yet since the invention of the wheel humans have lived in overwhelmingly happy and productive symbiosis with the technologies they have created. Despite our ongoing appetite for scare stories, we will continue to embrace such innovations as the primary source of improvements in our collective well being. In doing so we will come to see them as natural extensions of ourselves—indeed as enablers and enhancers of our humanity—rather than as something artificial or alien. A life lived partly in virtual reality will be no less real than one viewed through a pair of contact lenses; a person with a computer inserted in their brain, rather than merely into their pocket, will be seen as no less human than someone with a pacemaker; and stepping into a vehicle or aircraft without a human at the controls will come to be seen not as reckless but as benignly reassuring. We are surely not too far from the day when Edge will receive its first contribution from a genetically enhanced author or an artificial intelligence. That too will be big news, but not for long.

Thus, humanity is subject to two inexorably rising tides: a scientific and technological one in which the magical eventually becomes mundane, and a psychological and social one in which the unthinkable becomes unremarkable. Individual news stories will continue to make noise, but will come and go like breaking waves. Meanwhile beneath them, inconspicuous in their vastness, the twin tides of technological and social change will continue their slow but relentless rise. For generations to come they will go on testing and extending the boundaries of human knowledge and acceptance, causing both trepidation and wonder. This will be the real story of our species and our age.

The annual question might simply have been, “what recent scientific developments are important and will continue to be important?” But this year’s annual question is about recent important scientific news. The word “news” changes the question and the potential answers. It adds social and psychological implications. Now we have to consider not just the impact of the scientific development within a scientific field, but also its impact on people’s lives and on government policy decisions. We have to take into consideration the motivation or reason for it becoming a news story and the audience it is aimed at. What makes certain recent scientific developments “newsworthy”? And why will certain scientific stories remain newsworthy for years to come?

That being said, there seems to be a dichotomy in the scientific news stories that we see most frequently today. The first group includes news stories about exciting and/or useful scientific developments (many of which are being highlighted on edge.org in response to the annual question). The second group includes news stories about actions taken that blatantly go against scientifically acquired knowledge and that even demonstrate a skepticism of the value of science.

A few of the more well known stories from this second group include: parents refusing to vaccinate their children, the scare over genetically modified foods, the movement against teaching evolution in schools, skepticism about global warming, and a fear of fluorinating water. Both groups have stories that will remain in the news for years to come.

One would think that the first group of news stories, along with other scientific writing aimed at a general audience (including the writings of the third culture), should work to reduce the number of new stories in the second group. Unfortunately this does not seem to be the case. In this modern era of the Internet and cable TV the quantity of positive scientific news has certainly increased, but so has the quantity of negative scientific stories. One might guess that the positive stories in the first group are primarily read by those who already believe in science, and that the negative stories in the second group are finding an audience susceptible to the anti-science message. We need to find ways of bringing well-explained positive stories from the first group to a broader audience. And we need well-explained rebuttals written to counter the negative stories in the second group.

We must also remember that skepticism is understandable considering how complex some of the science is that people are being asked to believe. Climate change/global warming is a good example. It is one of the most complex subjects being worked on by scientists today. It involves physics, chemistry, biology, geology, and astronomy, and relies heavily on proxy data (based on isotope ratios of various elements captured in ice cores, sediment cores, corals, etc.) to provide us with records of temperature, carbon dioxide, methane, sea level, ice sheets, and other parameters needed to describe climate change (including ice ages) over millions of years. Such data are critical for validating computer climate models (run in the past), which are needed to predict future climate change. We try to explain to the non-scientist as best we can how the climate system works, and likewise how vaccines work, why genetically modified food is just as safe as food derived by Mendel-based breeding methods, why fluorinated water is safe, how natural-selection based evolution works, and so on. But given that we may not succeed with many readers, we must at least get across the incredible amount of careful meticulous work and theory testing carried out by thousands of scientists to come to each of these conclusions.

The key question is, how can we use the positive science news stories of the first group, along with third culture writings, to change the minds of some of the audience members of the second group being hit with the negative skeptical science news stories? Over the last few decades we have made such great progress in scientific discoveries and in transitioning those discoveries into practical applications that improve and save lives. But perhaps we do not do a good enough job in explaining the transitioning part of the process, and in explaining the great positive impact of science on all our lives. If the skeptics could ever gain a full appreciation of the fact that their transportation, their means to communicate with each other, everything in their homes, at their jobs and in between, all came originally from science, would that make a difference in their thinking? Would they be willing to look at a subject a bit more objectively? Many (most?) might still be too heavily influenced by biases from religious beliefs and the propaganda of special interest groups to change their minds. In this Internet cable-news driven world the negative influences are everywhere. One can only hope that at least some of our skeptical elected officials might slowly modify their views.

In writing scientific news, more emphasis should be put on the way science has made modern life possible. If only we could put “Science made this possible” at the end of every scientific story, every technology story, and every story about our everyday activities. If only we could put “Science made this possible” signs on every appliance, drug, car, computer, game machine, and other necessities of life that people buy. Maybe that might eventually make a difference.

The end of 2015 coincided with the birth of Einstein’s general theory of relativity, which the great man presented to the Prussian Academy of Sciences in a series of four lectures in the midst of World War I. Widely regarded as the pinnacle of human intellectual achievement, "general relativity," as it came to be known, took many years to be well tested observationally. But today, after decades of thorough investigation, physicists have yet to find any flaw with the theory.

Nevertheless, one key test remains incomplete. Shortly after Einstein published his famous gravitational field equations, he came up with an intriguing solution of them. It describes ripples in the geometry of spacetime itself, representing waves that travel across the universe at the speed of light. The detection of these gravitational waves has been an outstanding challenge to experimental physics for several decades. Now, that long search seems to be nearing its culmination.

In the last few months, a laser system designed to pick up the passage of gravitational waves emanating from violent astronomical events has been upgraded, and rumors abound that it has already "seen something." The system concerned, called Advanced Ligo (for Laser Interferometer Gravitational Observatory), uses laser beams to spot almost inconceivably minute gravitational effects. In Europe, its counterpart, Advanced Virgo, is also limbering up. Advanced Ligo and Advanced Virgo are refinements of existing systems that have proved the technology but lack the sensitivity to detect bursts of gravitational waves from supernovas or colliding neutron stars on a routine basis. The stage is now set to move to that phase.

The detection of gravitational waves would not merely provide a definitive test of Einstein’s century-old theory, it would serve to open up a whole new window on the universe. Existing conventional telescopes range across the entire electromagnetic spectrum, from radio to gamma rays. Ligo and Virgo would open up an entirely new spectrum and with it an entirely new branch of astronomy, enabling observations of black hole collisions and other cosmic exotica.

Each time a new piece of technology has been used to study the universe, astronomers have been surprised. Once gravitational astronomy is finally born, the exploration of the universe through gravitational eyes will undoubtedly provide newsworthy discoveries for decades to come.

Over the past decade, with the discovery of optogenetics, neuroscience has thrown open a door that seemed closed forever. Before optogenetics, our ability to record the activity of cells in the brain was sophisticated, and we understood that mental states are represented in the distributed pattern of many cells. Our emotions, our thoughts, and what we perceive is the activity of millions of cells. What we lacked was the ability to trigger a similar state in the brain on command. Neuroscience was a spectator of the mind—not an actor. With the advent of optogenetics this is currently changing.

Optogenetics is a surprising new field of biotechnology that gives us the means to transform brain activity into light and light into brain activity. It allows us to introduce fluorescent proteins into brain cells to make cells glow when they are active—thereby transforming neural activity into light. It also allows us to introduce photosensitive ion channels into neurons, so that shining light on the cells triggers activity or silences neurons at will—thereby transforming light into neural activity. Combined with modern technologies to record light from neurons deeper and deeper in the brain, and to guide light onto individual neurons, we have crossed a frontier that only a decade ago seemed far far away: the ability not only to record the distributed patterns of brain activity that make up our percepts, thoughts, and emotions but, for the first time, to selectively recreate arbitrary states in the brain—and hence, the mind.

A small number of experiments have demonstrated the potential of this technique. For instance, mice were made to experience fear. Using optogenetics, the pattern of neural activity that was triggered during the original experience was later reactivated, and the mice froze in fear once again. Neuroscience has become a protagonist. The science fiction scenario of "total recall," in which Arnold Schwarzenegger was implanted with memories he never had, now becomes tangible. In another set of experiments, the activity of cells in one animal's brain was recorded and imposed on corresponding cells in the brain of another animal's that was then able to take decisions based on what the other animal was feeling.

I foresee that the ability to measure and recreate brain activity at the level of specific neurons at will is about to transform us in ways that no other invention ever has. The invention of fire, of the wheel, of antibiotics or the Internet changed how we live our lives in profound ways. It made our lives safer, more comfortable, and more exciting. But they have not changed who we are. Being able to record and manipulate brain activity will change who we are. It will serve as an interface through which computers can become part of our brain, and through which our brains could directly interface with each other.

When we observe a baby grow into a child, we witness how profoundly a person changes while connections in her brain allow her to tap into the resources of new brain regions. Soon, for some of us, this process will continue beyond the confined space of our body while optogenetic-like technologies will allow our minds to encompass the world of computers. Who will we become? What will the world look or feel like sensed directly not only with our six senses, but with all the sensors of the Internet of things? What would negotiations for the world climate feel like if we could directly connect with the brains of the people around us and experience the ultimate form of empathy? How will our societies deal with a transition phase in which neuro-enhancement will be affordable to some of us and not to others? In which some will have amazing powers of thought while others will remain confined to their own brain?

A remarkable thing about any piece of news—scientific or otherwise—is that it’s very difficult to determine its longevity. Surely the most important news is long-lasting, and in turn generates further news.

A prime example of "important" scientific news that turned out to be mistaken is the "discovery" of N-rays by the physicist René Blondlot in 1903. This was hailed as a major breakthrough, and led rapidly to the publication of dozens of other papers claiming to confirm Blondlot’s findings. Yet N-rays were soon discredited, and are now referred to primarily as an example of a phenomenon in perceptual psychology—we perceive what we expect to perceive.

On the other hand, scientists often hugely underrate the practical importance of their discoveries so that news about them does not begin to do justice to their implications. When Edison patented the phonograph in 1878, he believed it would be used primarily for speech, such as for dictation without the aid of a stenographer, for books that would speak to blind people, for telephone conversations that could be recorded, and so on. Only later did entrepreneurs realize the enormous value of recorded music. But once this happened the music industry developed rapidly.

The laser provides another example of the initial underrating of the practical implications of a scientific discovery. When Schawlow and Townes published their seminal paper describing the principle of the laser in Physical Review Letters in 1958, this produced considerable excitement in the scientific community, and eventually won them the Nobel Prize. However, neither these authors nor others in their group predicted the enormous and diverse practical implications of their discovery. Apart from their many uses in science, lasers enabled the development of fast computers, target designation in warfare, communication over very long distances, space exploration and travel, surgery to remove brain tumors, and numerous everyday uses such as bar code scanners in supermarkets, among other things. Yet soon after his theoretical discovery, Arthur Schawlow frequently expressed strong doubts about the laser’s practical implications, and several times quipped that the only use that might be found for this device was for safecracking by burglars. However, advances in laser technology continue to make news to this day.

Surveying the landscape of scientific and technological change, there are a number of small and relatively steady advances that have unobtrusively combined to yield something startling, almost without anyone noticing. Through a combination of computer hardware advances (Moore’s Law marching on and so forth), ever-more sophisticated algorithms for solving certain mathematical challenges, and larger amounts of data, we have gotten something new: really good weather prediction.

According to a recent paper on this topic, “Advances in numerical weather prediction represent a quiet revolution because they have resulted from a steady accumulation of scientific knowledge and technological advances over many years that, with only a few exceptions, have not been associated with the aura of fundamental physics breakthroughs.” Despite these advances seeming to be profoundly unsexy, these predictive systems have yielded enormous progress. Our skill at forecasting the weather has grown significantly over the past few decades—something that will change how we think about the enormously complex system of the weather. Forecasting ability has been increasing in accuracy by about an additional day per decade for those made several days into the future.

This is intriguing and important for a number of reasons. First, understanding weather is vital for a huge number of human activities, from transportation to improving agricultural output, to even managing disasters. Being able to foretell what the weather holds affects nearly every aspect of our lives.

But there’s a potentially bigger reason. While I am hesitant to extrapolate from the weather system to other complex systems, including many that are perhaps much more complex (such as living organisms or entire ecosystems), this development should give us some hope. The fact that weather prediction has improved through a combination of technological advancement and scientific and modeling innovations means that other problems we might think to be ever-unsolvable needn’t be this way. While we might never fully handle incredibly complicated systems, they are not completely beyond our grasp.

This news of a “quiet revolution” in weather prediction might then be a touchstone for how to think about predicting and understanding complex systems: never say never when it comes to complete intractability.

The two rules for what lived and died, over the long term, were pretty clear: natural selection and random mutation. But over the last century or two, and especially over the last decade, humans fundamentally altered these rules. Life as we know it will undergo rapid and accelerating change; it will be redesigned and will diverge, especially post May 2014.

Already, we largely determine what lives and dies on half the surface of the planet, anywhere we have built cities, suburbs, parks, farms, ranches. That makes both cornfields and beautiful gardens some of the most unnatural places ever designed. Nothing lives and dies there except what we want, where and when we want—orderly rows of plants that please us. All else is culled. (But leave this fallow and untended for a couple of years and you will begin to see what is driven by the other evolutionary system, natural selection.)

In redesigning our own environment, we create and nurture completely unnatural creatures. These range from miniature pigs the size of Chihuahuas and corn that cannot self-replicate to the big tom turkeys that breed our Thanksgiving meals, animals so grossly exaggerated that they are unable to copulate naturally and require artificial insemination. No humans, no big Thanksgiving turkey breasts; today’s beasts are, on average, 225% larger than they were in the 1930s.

Without human intervention most of the creatures that live around you would simply have been selected out. (Let a Lhasa Apso loose on the African plain and watch what happens.) Same is true of humans; in an all-natural environment, most of humanity would not be alive. Just unnaturally selecting out microbes and viruses like smallpox, polio, bubonic plagues, and most infections, means billions more get to live.

As we practice extreme human intervention and alter the course of natural selection we create a parallel evolutionary track, one whose rules and outcomes depend on what we want. Life begins to diverge from what nature would design and reward, absent our conscious and unconscious choices. A once unusual observation, that during the Industrial Revolution black moths in London survived better than white moths because they were better camouflaged in a polluted environment, has now become the norm. Life around us is now primarily black-moth-like-adaptations to our environments: cute dogs, cats, flowers, foods. We have so altered plants, animals, and bacteria that to survive they have to reward us, or at least be ignored by us.

These two parallel evolutionary systems, one driven by nature, the other by humans, both breed, expand, and evolve life, driving parallel and diverging evolutionary trees. The divergence between what nature would choose and what we choose gets ever larger. Many of the life forms we are so accustomed to, and dependent on, would disappear or radically modify in our absence.

But the true breakpoint began over the past few decades, when we began not just choosing how to breed but how to rewrite the code of life itself. In the 1970s and 1980s, biotechnology gave us the ability to insert all kinds of gene instructions. Random mutation is gradually being displaced by intelligent design. By 2000 we were decoding entire genomes and applying this knowledge to alter all kinds of life forms. Today’s high schoolers can spend $500 and alter life code using methods like CRISPR; these types of technologies can alter all subsequent generations, including humans.

In May 2014, a team of molecular biologists led by Floyd Romesberg created a new genetic code, a self-replicating system that codes life forms using chemically modified DNA. This is a BFD; insofar as we know, for 4 billion years all life on this planet replicated using the four known base pairs of DNA (ATCG). Now we can swap in other chemicals. This third evolutionary logic-tree of life would initially be completely human designed-driven and could rapidly diverge from all known life. In theory scientists could begin to breed plants and animals with a very different genetic makeup from that of any other creature on the planet. And these new life forms may be immune to all known viruses and bacteria.

Finally, if we discover life on other planets, something that seems increasingly likely, the biochemistry of these other life forms is likely to further increase the variety and options that life on Earth can take, providing life-designers with entirely new instruments and ideas to program-redesign existing life forms so they adapt to different environments.

So the biggest story of the next few centuries will be how we begin to redesign life forms, spread new ones, develop approaches and knowledge to further push the boundaries of what lives where. And as we deploy all this technology we will see an explosion of new life forms—something that could make the Cambrian explosion look tame.

Life is expanding and diverging. Humans will not be immune to this trend. We already coexisted and interbred with other versions of hominins; it is normal and natural for there to be different versions of ourselves walking around. Soon we could return to this historically normal state but with far more, and perhaps radically different, versions of ourselves. All of which may lead to just a few ethical, moral, and governance challenges.

Many cosmologists now think our spatial universe is infinite. That’s news. It was only this year that I heard about it. I don’t get out as much as I used to.

Thirty years ago it was widely believed that our spatial universe is the finite 3D hypersurface of a 4D hypersphere—analogous to being the finite 2D surface of a 3D sphere. Our underlying hypersphere was supposedly born, and began expanding, at the Big Bang. And eventually our hypersphere was to run out of momentum and collapse back into a Big Crunch—which might possibly serve as the seed for a new Big Bang. No yawning void of infinity, and no real necessity for a troublesome initial point in time. Our own Big Bang itself may have been seeded by a prior Big Crunch. Indeed, we could imagine an endless pearl-string of successive hyperspherical universes. A tidy theory.

But then experimental cosmologists found ways to estimate the curvature of our space, and it seems to be flat, like an endless plane, not curved like the hypersurface of a hypersphere. At most, our space might be "negatively curved," like an endless hyperbolic saddle shape, but then it’s probably infinite as well.

If you’re afraid of infinity, you might say something like this: "So, okay, maybe we’re in a vast infinite space, but it’s mostly empty. Our universe is just a finite number of galaxies rushing away from each other inside this empty infinite space—like a solitary skyrocket exploding and sending out a doomed shower of sparks." But many cosmologists say, no, there are an infinite number of galaxies in our infinite space.

Where did all those galaxies come from? The merry cosmologists deploy a slick argument involving the relativity of simultaneity and the inflationary theory of cosmic inflation—and they conclude that, in the past, there was a Big Bang explosion at every single point of our infinite space. Flaaash! An infinite space with infinitely many galaxies!

Note that I’m not talking about some shoddy "many universes" theory here. I hate those things. I’m talking about our good old planets-and-suns single universe. And they’re telling us it goes on forever in space, and on forever into the future, and it has infinitely many worlds. We aren’t ever going to see more than a few of these planets, but it’s nice to know they’re out there.

So, okay, how does this affect me in the home?

You get a sense of psychic expansion if you begin thinking in terms of an infinite universe. A feeling of freedom, and perhaps a feeling that whatever we do here does not, ultimately, matter that much. You'd do best to take this in a "relax" kind of way, rather than in an "it’s all pointless" kind of way.

Our infinite universe’s inhabited planets are like dandelion flowers in an endless meadow. Each of them is beautiful and to be cherished—especially by the little critters who live on them. We cherish our Earth because we’re part of it, even though it’s nothing special. It’s like the way you might cherish your family. It’s not unique, but it’s yours. And maybe that’s enough.

I know some of you are going to want more. Well, as far as I can see, we’re living in one of those times when cosmologists have no clear idea of what’s going on. They don’t understand the start of the cosmos, nor cosmic inflation, nor dark energy, nor dark matter. You might say they don’t know jack.

Not knowing jack is a good place to be, because it means we’re ready to discover something really cool and different. Maybe next year, maybe in ten, or maybe in twenty years. Endless free energy? Antigravity? Teleportation? Who can say. The possibilities are infinite and the future is bright.

In their book, Virtuous Violence: Hurting and Killing to Create, Sustain, End, and Honor Social Relationships, moral psychologist Tage Rai at Northwestern and psychological anthropologist Alan Fiske at UCLA sketch the extent to which their work shows that violent behavior among human beings is often not a breach of moral codes but an embodiment of them.

In a way, we all know this, by way of the exceptions we permit. Augustine’s theory of the Just War arose because his god demonstrably approved of some wars. When Joshua fought the battle of Jericho, he had divine approval, "thou shalt not kill" be damned. To Augustine’s credit and others in that tradition, the Just War theory represents hard work to resist as much licit violence as possible. To their discredit, it represents their decision to cave in to questionable evidence and put a stamp of approval on slaughter. (Am I hallucinating to remember a small thumbnail woodcut of Augustine in the margin of a Time essay on the debates over the justice of the Vietnam War? If my hallucination is correct, then I remember shuddering at the sight.)

And certainly we have plenty of examples closer to date: Mideast terrorists and anti-abortion assassins are flamboyant examples, but elected statesmen—American no less than from countries we aren’t so fond of—are no less prone to pull the trigger on killing with exact justifications based in the soundest moral arguments. We glance away nervously and mutter about exceptions. What if the exceptions are the rule?

If the work of Rai and Fiske wins assent, it points to something more troubling. The good guys are the bad guys. Teaching your children to do the right thing can get people killed. We have other reasons for thinking the traditional model of how human beings work in ideal conditions (intellectual consideration of options informed by philosophical principles leading to rational action) may be not just flawed but downright wrong. Rai/Fiske suggest that the model is not even sustainable as a working hypothesis, or faute de mieux, but is downright dangerous.

Yesterday, no extraterrestrials landed! Or the day before! Or, despite many claims to the contrary, in any earlier period of human history. Or Earth history.

This is odd. There are several hundred billion stars in our galaxy and at least a hundred billion galaxies in our Universe. In the last twenty years, astronomers have detected lots of planets around nearby stars, so we know planets are common. In fact, there could be tens of billions, or even a hundred billion Earth-like planets in our galaxy alone.

It’s hard not to think that a lot of these Earth-like planets (a few million perhaps?) may have had histories a bit like our Earth. They may have spawned living organisms. On Earth, we have found life in many extreme environments, from deep sea oceanic vents (where the current record-holder can survive at 120° C.), to the inside of rocks, where they have to live very very slowly in order to survive. Endospores can temporarily stop living (well, metabolism ceases) until things improve. Some bacteria may have jumped from Mars to Earth. So, life can exist in a wide range of environments, and today, many astrobiologists believe that life might have existed on Mars and Venus, and could exist even now on some of the moons of Jupiter and Saturn, such as Io and Europa, which have lots of ice. All in all, it’s beginning to seem that life of some kind could be common in the Universe. It may be that the Universe is quite bio-friendly.

If Simon Conway Morris and others are right, and there is a limited number of pathways along which life can evolve, then any organisms that exist on other planetary systems may have evolved in ways not too dissimilar to the organisms on our Earth. Evolution may have converged on similar solutions in different star systems. Perhaps multi-cellular organisms have evolved many times. Perhaps many had ways of detecting light waves (eyes?); and perhaps many developed ways of computing or thinking (brains?). Our galaxy is 13 billion years old, and most of its stars are older than our Sun, so most of its planetary systems should be older than our Earth, which means they would have had much longer to evolve complex life forms.

Here on Earth, life got going more or less as soon as our young planet was cool enough to have liquid water. That’s fast, and hints that simple forms of life may be common. Four billion years later, large, intelligent creatures have appeared, and lots of them. One of those species crossed a critical threshold when it evolved such a powerful form of language that its members began to share their ideas and accumulate more and more information from generation to generation. As a result of its ability to learn collectively, that species (us) has built an astonishing store of knowledge, which enables us to control more and more of our environment, until now we dominate the planet. We have become a planet-changing species and we now live in what many scholars call the "Anthropocene Epoch." We’ve even launched a few of us short distances into space, and sent robots throughout our solar system.

On planets where evolution began millions of years earlier than on Earth, you’d think evolution might have gone well past the crucial threshold of collective learning, past the production of a planet-changing species, and on, perhaps, to the point of colonizing nearby star systems. Could there be thousands of planets with species capable of collective learning? We can’t know, but such an estimate is not impossible, and many of these planets could be orbiting the 4,500 star systems within 60 light-years of our Earth that make up our galactic neighborhood.

So where are the extraterrestrials? This was Fermi’s famous question. The SETI program has been scanning the heavens for evidence of alien life since 1960. We haven’t seen them. We haven’t heard them either, or detected any other signs of their existence. Frank Drake, inventor of the "Drake equation," which lists the factors we must take into account to estimate the likelihood of encountering other species like ourselves, thought that one of the crucial factors might be how long planet-changing species like us could survive.

And there’s the rub. We are so clever that we have invented weapons that could ruin the biosphere in a few hours; and our energy-hungry civilizations seem to be degrading the biosphere and the climate-systems on which we depend. Is it possible that planet-changing species like us never get past this stage? Do they all hit a wall when they reach their local Anthropocene? If so, such species may last for a few centuries or a millennium or two, and then flicker out, perhaps after retreating to impoverished niches where they eke out a miserable existence before going extinct. That would mean that even if planet-changing species—species capable of telling stories and jokes, of painting and dancing, and building pyramids and space ships—are quite common, they would all self-destruct. That would solve Fermi’s problem!

Or perhaps some other planet-changing species actually learnt their lesson, maybe after a few self-inflicted catastrophes. Perhaps they decided not to aim too high, not to try to dominate their planet or their solar system or neighboring star systems, but to live more sociably with their home planet and the other organisms that surrounded them, after realizing this was the only way of surviving. Perhaps we don’t see them because, like Candide at the end of Voltaire’s novel, they are all happily cultivating their own gardens. That would also solve Fermi's problem!

The human species has successfully dealt with twenty or more distinct episodes of global warming, surviving, if not prevailing, but in circumstances that no longer exist.

There is no real difficulty in identifying the most important news of 2015. Global warming is the news that will remain news for the foreseeable future, because our world will continue to warm at a rate that has never been seen before, at least at the moment without a foreseeable end. Writing as a paleoanthropologist, despite the fact that the past is usually a poor model for understanding the present, this time paleoanthropology brings a quite different perspective to the news because global warming has happened before, perhaps twenty or more distinct episodes of warming during the time our genus—Homo—has existed, with important, sometimes quite significant, effects on human evolution. The human species has survived global warming in the past, indeed persisted in the face of global warming, but paleoanthropology is a comparative science and comparing most past episodes of global warming to the global warming in today’s news leads me to question whether (and how) we may survive this one. And this is not solely because of a point the news also recognizes, the rate of temperature change is much faster than humans have ever experienced before.

Prior episodes of significant global warming within the Pleistocene, more-or-less the last 2 million years, have invariably followed cooling periods with glacial advances. During the Pleistocene, the human lineage, within the genus Homo, successfully adapted to changing environments (including climate) and evolved to take advantage of the opportunities afforded by the changes, even as populations of Homo reacted to the constraints environmental changes created. Human populations evolved diverse adaptations to the different climates and ecological circumstances they encountered, but the improvements in communication skills, planning depth, and retention of deep history with tales, poems, song, and other aspects of cultural behavior dispersed throughout humanity.

At any particular time the Pleistocene world population was quite small, 1 to 2 million is estimated during all but the most recent episodes of cooling and warming cycles, half or more living in Africa. Scarce on the ground, with little ecological impact and with vast habitable areas unoccupied by human groups, the human reactions to periods of global warming often were simply population movements. These had important consequences over the course of human evolution because our particular brand of evolutionary change—the unique human evolutionary pattern—began with the initial and ongoing geographic dispersals of human populations. In many mammalian species, significant range expansion such as the human one resulted in geographic isolation for many groups, and the formation of subspecies and ultimately of species. In humans these processes were mitigated by continuing population interconnections created by gene flow, in some cases the result of population movements, and in others because expanding human populations grew to encounter each other. The unique human evolutionary pattern was created as adaptive genes and behaviors, under selection, spread throughout the human range. Genetic changes adaptive for the entire human species were able to disperse throughout it, no matter how different individual populations may have become, because population contacts allowed it and natural selection promoted it. Thus, the unique human pattern of evolution is based on continued population mixtures, and as some populations merged and others became extinct, continued replacements.

But this long-lasting pattern has been disrupted as humans gained increasing control of their food resources and human populations began the accelerated increase in number that is so evident today. This is recent, so recent that a rapidly growing humanity is yet to have encountered significant climate change, until now. Today’s headlines make it clear that the change we are encountering is global warming. It is not at all evident that the adaptive successes of the past, as the human species successfully reacted to many instances of global warming, guarantee a successful human reaction to the changes coming upon us today. The world is quite different. Adaptive strategies that underlay a successful strategy for the human species in the past may no longer be possible; a vastly larger number of humans probably preclude similar success from the same strategies, even without considering the rapidity of the climate changes humanity is encountering.

The fact is that the present is not a simple extension of the past, the conditions are radically different and the strategies that promoted a successful balance of population variation within the human species and a successful adaptation for all populations throughout the Pleistocene may, today, create competition between human populations at a level that could make the lives of the survivors quite unpleasant.

Of course, nothing like this is inevitable, or even necessarily probable, but its possibility looms large enough to be taken seriously. We need to learn from the past without trying to repeat it.

In the last couple of years toddlers and even babies have begun to be able to use computers. This may seem like the sort of minor news that shows up in the "lifestyle" section of the paper and in cute you-tube videos. But it actually presages a profound change in the way human beings live.

Touch and voice interfaces have only become ubiquitous very recently—it’s hard to remember that the iPhone is only eight years old. For grown-ups, these interfaces are a small additional convenience. But they completely transform the way that young children interact with computers. For the first time, a toddler can directly control a smart phone or tablet.

And they do. Young children are fascinated by these devices and they are remarkably good at getting them to do things. In recognition of this, in 2015, the American Academy of Pediatrics issued a new report about very young children and technology. For years the Academy had recommended that children younger than two should have no access to screens at all. The new report recognizes that this recommendation has become completely impracticable. It focuses instead, sensibly, on ensuring that when young children look at screens, they do it in concert with attentive adults, and that adults supervise what children see.

But this isn’t just news for anxious parents, its important for the future of the entire human species. There is a substantial difference between the kind of learning we do as adults, or even as older children, and the kind of learning we do before we are five. For adults, learning mostly requires effort and attention; for babies, learning is ubiquitous and automatic. Grown-up brains are more "plastic" than we once thought, (neural connections can rewire) but very young brains are far more plastic—young children’s brains are designed to learn.

In the first few years of life we learn about the way the physical, biological, and psychological world work. Even though our everyday theories of the world depend on our experience, by the time we’re adults we simply take them for granted—they’re part of the unquestioned background of our lives. When technological, culturally specific knowledge is learned early it becomes part of the background too. In our culture children learn how to use numbers and letters before they are five, in rural Guatemala, they learn how to use a machete. These abilities require subtle and complicated knowledge, but it’s a kind of knowledge that adults in the culture hardly notice (though it may startle visitors from another culture).

Until now, we couldn’t assume that people would know how to use a computer in the way we assume they know how to count. Our interactions with computational systems depended on first acquiring the skills of numeracy and literacy. You couldn’t learn how a computer worked without first knowing how to use a keyboard. That ensured that people learned about computers with relatively staid and inflexible old brains. We think of millennial high-school tech whizzes as precocious "digital natives." But even they only really began to learn about computers after they’d reached puberty. And that is just the point when brain plasticity declines precipitously.

The change in interfaces means that the next generation really will be digital natives. They will be soaked in the digital world and will learn about computers the way previous generations learned language—even earlier than previous generations learned how to read and add. Just as every literate person’s brain has been reshaped by reading, my two-year-old granddaughter’s brain will be reshaped by computing.

Is this a cause for alarm or celebration? The simple answer is that we don’t know and we won’t for at least another twenty years, when today’s two-year-olds grow up. But the past history of our species should make us hopeful. After all, those powerful early learning mechanisms are exactly what allowed us to collectively accumulate the knowledge and skill we call culture. We can develop new kinds of technology as adults because we mastered the technology of the previous generation as children. From agriculture to industry, from stone tools to alphabets to printed books, we humans reshape our world, and our world reshapes our brains. Still, the emergence of a new player in this distinctively human process of cultural change is the biggest news there can be.

One out of every two people will have to deal with a diagnosis of cancer during their lifetimes. The 10 percent of cancers that arise in genetically "high risk" groups alone represent less than 1 percent of the total population in the US, but cost a staggering $15 billion to treat annually.

Despite decades of research, the Holy Grail of a cure still eludes us, in part because of the fundamental, unstable nature of cancer—it easily produces variant cells that resist chemotherapies and this often results in relapse. Cancer is also difficult because different cancer types differ considerably in their biology, meaning that a single drug is unlikely to be effective against more than one or a few cancer types. Finally, even within a cancer type, different patients can have differences in how their cancers react to a given drug. What this all means is that "one drug that cures all," is not one drug, and unfortunately for metastatic cancer, often not a cure.

The problem is in many ways encapsulated by the following observation: for late-stage cancers, which are the most difficult to treat, most new drugs are considered a success if they extend life for several weeks or months. The limited or disappointing results of many chemotherapies has led to concerted efforts to identify the Achilles’ heel, or rather heels, of cancers.

If it’s any litmus test of where the most promising discoveries are being made, just read the titles any week of the world’s most prestigious scientific journals and parallel coverage in the popular press: it’s all about immunotherapies.

The idea makes sense: harness a patient’s own natural mechanisms for eliminating diseased cells, or give the patient man-made immune system components to help specifically target malignancies. This is certainly better, all else being equal, than injections of toxic drugs. Consider that the basic challenge of traditional chemotherapies is that they affect both cancerous and, to some extent healthy cells, meaning that for the drugs to work, doses must be carefully established to kill or arrest the growth of cancer cells, while keeping the patient alive. That is, the more drug, the more the cancer regresses, but the higher the chance of side effects or even patient death. This is problematic because many patents cannot withstand the doses of chemotherapies that are most likely to cure them, and even if they are, exposing rapidly dividing, mutation-prone cancer cells will strongly select for resistance to the therapy. Darwin got it right in his theory of natural selection, and his insights help us understand why remission is often followed by relapse.

Employing our own immune systems has intuitive appeal. Our bodies naturally use immunoediting and immune surveillance to cull diseased cells. However, the tumor microenvironment is a complex, adaptive structure that can also compromise natural and therapy-stimulated immune responses. This past year has seen important milestones. For example, based on promising clinical trials, the FDA recently approved a combination of two immunotherapies (Nivolumab and Ipilimumab) for metastatic melanoma. What one is not able to accomplish, the other is; this not only reduces tumor size, but is expected to result in less evolved resistance to either drug. This same idea of using combinations can be applied to immunotherapies together with many of the more traditional radiotherapies, chemotherapies, and more recent advances in targeted therapies.

Currently more than forty clinical trials are being conducted to examine effects of immunotherapies on breast cancer, with the hope that within a decade such therapies, if promising, can reduce or eliminate these cancers in the 1 in 8 women that currently are affected during th